diff --git a/articles/running-monlix.html b/articles/running-monlix.html index a35c20c0..1c1a5b7c 100644 --- a/articles/running-monlix.html +++ b/articles/running-monlix.html @@ -100,7 +100,7 @@

or use monolixControl(runCommand="monolix"). If needed, I prefer @@ -185,10 +185,10 @@

Step 1: Run a nlmixr2 in M #> → Calculating residuals/tables #> ✔ done #> → compress origData in nlmixr2 object, save 27560 -#> ℹ monolix parameter history needs expoted charts, please export charts +#> ℹ monolix parameter history needs exported charts, please export charts

This fit issues an informational tidbit -

This will automatically be generated as well when @@ -213,8 +213,8 @@

Step 1: Run a nlmixr2 in M #> #> ── Time (sec fit$time): ── #> -#> setup table compress other -#> elapsed 0.003452 0.084 0.01 5.166548 +#> setup table compress other +#> elapsed 0.00353 0.093 0.009 5.08647 #> #> ── Population Parameters (fit$parFixed or fit$parFixedDf): ── #> @@ -291,7 +291,7 @@

#> → calculate jacobian #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate sensitivities -#> [====|====|====|====|====|====|====|====|====|====] 0:00:01 +#> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate ∂(f)/∂(η) #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate ∂(R²)/∂(η) @@ -351,7 +351,7 @@

Optional Step 3: Use

-

Notes about Monlix data translation +

Notes about Monolix data translation

The input dataset expected to be compatible with rxode2 or nlmixr2. This dataset is then converted to Monolix @@ -369,15 +369,15 @@

Notes about Monlix data translation dosing:

  • Bolus/infusion uses depot() and adds modeled lag -time (Tlag) or bioavailibilty (p) if +time (Tlag) or bioavailability (p) if specified

  • Modeled rate uses depot() with Tk0=amtDose/rate. babelmixr2 also adds modeled -lag time (Tlag) or bioavailibilty (p) if +lag time (Tlag) or bioavailability (p) if specified

  • Modeled duration uses depot() with Tk0=dur, also add adds modeled lag time (Tlag) -or bioavailibilty (p) if specified Turning off a +or bioavailability (p) if specified Turning off a compartment uses empty macro

diff --git a/articles/running-nonmem.html b/articles/running-nonmem.html index cf94a370..a3dc2ccb 100644 --- a/articles/running-nonmem.html +++ b/articles/running-nonmem.html @@ -231,7 +231,7 @@

Optional Step 2: Recover a #> → compiling EBE model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done -#> rxode2 2.0.13.9000 using 1 threads (see ?getRxThreads) +#> rxode2 2.0.14.9000 using 1 threads (see ?getRxThreads) #> no cache: create with `rxCreateCache()` #> → Calculating residuals/tables #> ✔ done @@ -279,7 +279,7 @@

Optional Step 2: Recover a #> ── Time (sec $time): ── #> #> setup table compress NONMEM -#> elapsed 0.029967 0.056 0.017 320.27 +#> elapsed 0.027612 0.058 0.015 320.27 #> #> ── Population Parameters ($parFixed or $parFixedDf): ── #> @@ -356,7 +356,7 @@

Option #> → calculate jacobian #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate sensitivities -#> [====|====|====|====|====|====|====|====|====|====] 0:00:01 +#> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate ∂(f)/∂(η) #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → finding duplicate expressions in inner model... @@ -381,7 +381,7 @@

Option #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> calculating covariance matrix -#> [====|====|====|====|====|====|====|====|====|====] 0:00:09 +#> [====|====|====|====|====|====|====|====|====|====] 0:00:08 #> Warning in foceiFitCpp_(.ret): using R matrix to calculate covariance, can #> check sandwich or S matrix with $covRS and $covS #> Warning in foceiFitCpp_(.ret): gradient problems with covariance; see @@ -470,7 +470,7 @@

Optiona #> ── Time (sec f2$time): ── #> #> setup table compress NONMEM -#> elapsed 0.004675 0.055 0.017 505.59 +#> elapsed 0.004274 0.047 0.013 505.59 #> #> ── Population Parameters (f2$parFixed or f2$parFixedDf): ── #> diff --git a/news/index.html b/news/index.html index c17870a5..19ef4991 100644 --- a/news/index.html +++ b/news/index.html @@ -59,7 +59,7 @@

babelmixr2 0.1.1

CRAN release: 2023-05-27

diff --git a/pkgdown.yml b/pkgdown.yml index 2a844398..f665afbf 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -5,5 +5,5 @@ articles: running-monlix: running-monlix.html running-nonmem: running-nonmem.html new-estimation: new-estimation.html -last_built: 2023-08-27T04:12Z +last_built: 2023-10-22T02:53Z diff --git a/reference/as.nlmixr2.html b/reference/as.nlmixr2.html index dbd6f671..ed98fb73 100644 --- a/reference/as.nlmixr2.html +++ b/reference/as.nlmixr2.html @@ -236,7 +236,7 @@

Examples#> #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done -#> rxode2 2.0.13.9000 using 1 threads (see ?getRxThreads) +#> rxode2 2.0.14.9000 using 1 threads (see ?getRxThreads) #> no cache: create with `rxCreateCache()` #> → Calculating residuals/tables #> ✔ done @@ -254,7 +254,7 @@

Examples#> ── Time (sec $time): ── #> #> setup table compress NONMEM as.nlmixr2 -#> elapsed 0.032108 0.076 0.025 100.95 3.559 +#> elapsed 0.046263 0.071 0.023 100.95 3.472 #> #> ── Population Parameters ($parFixed or $parFixedDf): ── #> diff --git a/reference/pkncaControl.html b/reference/pkncaControl.html index f12ca2e3..bcb63db9 100644 --- a/reference/pkncaControl.html +++ b/reference/pkncaControl.html @@ -121,6 +121,10 @@

Arguments

Value

diff --git a/search.json b/search.json index 8a2a9d22..cb8a4703 100644 --- a/search.json +++ b/search.json @@ -1 +1 @@ -[{"path":"/articles/new-estimation.html","id":"create-a-nlmixr2est-method","dir":"Articles","previous_headings":"","what":"Create a nlmixr2Est() method","title":"Creating a New Estimation Method","text":"method input environment nlmixr2est UI object (see ?nlmixr2Est). output fit object.","code":""},{"path":"/articles/new-estimation.html","id":"create-a-control-method","dir":"Articles","previous_headings":"","what":"Create a control method","title":"Creating a New Estimation Method","text":"control method gives access controls required estimation.","code":""},{"path":"/articles/running-monlix.html","id":"step-0-what-do-you-need-to-do-to-have-nlmixr2-run-monolix-from-a-nlmixr2-model","dir":"Articles","previous_headings":"","what":"Step 0: What do you need to do to have nlmixr2 run Monolix from a nlmixr2 model","title":"Running Monolix","text":"use Monolix nlmixr2, need change data nlmixr2 dataset. babelmixr2 heavy lifting . need setup run Monolix. setup lixoftConnectors package Monolix, setup needed. Instead run Monolix command line grid processing (example) can figure command run Monlix (often useful use full command path set options, ie options(\"babelmixr2.monolix\"=\"monolix\") use monolixControl(runCommand=\"monolix\"). needed, prefer options() method since need set . also function prefer (cover using function ).","code":""},{"path":"/articles/running-monlix.html","id":"step-1-run-a-nlmixr2-in-monolix","dir":"Articles","previous_headings":"","what":"Step 1: Run a nlmixr2 in Monolix","title":"Running Monolix","text":"Lets take classic warfarin example. model use nlmixr2 vignettes : monolix run, can run nlmixr2 model using Monolix new estimation method: fit issues informational tidbit - monolix parameter history needs expoted charts, please export charts automatically generated well lixoftConnectors package generated recent version Monolix. don’t information important parameter history plots imported see plots. Just like NONMEM translation, monolixControl() modelName helps control output directory Monolix (specified babelmixr2 tries guess based model name based input). Printing nlmixr2 fit see: particular interest comparison Monolix predictions nlmixr predictions. case, believe also imply models predicting thing. Note model predictions close NONMEM Monolix use lsoda ODE solver. Hence small deviation expected, still gives validated Monolix model.","code":"pk.turnover.emax3 <- function() { ini({ tktr <- log(1) tka <- log(1) tcl <- log(0.1) tv <- log(10) ## eta.ktr ~ 1 eta.ka ~ 1 eta.cl ~ 2 eta.v ~ 1 prop.err <- 0.1 pkadd.err <- 0.1 ## temax <- logit(0.8) tec50 <- log(0.5) tkout <- log(0.05) te0 <- log(100) ## eta.emax ~ .5 eta.ec50 ~ .5 eta.kout ~ .5 eta.e0 ~ .5 ## pdadd.err <- 10 }) model({ ktr <- exp(tktr + eta.ktr) ka <- exp(tka + eta.ka) cl <- exp(tcl + eta.cl) v <- exp(tv + eta.v) emax = expit(temax+eta.emax) ec50 = exp(tec50 + eta.ec50) kout = exp(tkout + eta.kout) e0 = exp(te0 + eta.e0) ## DCP = center/v PD=1-emax*DCP/(ec50+DCP) ## effect(0) = e0 kin = e0*kout ## d/dt(depot) = -ktr * depot d/dt(gut) = ktr * depot -ka * gut d/dt(center) = ka * gut - cl / v * center d/dt(effect) = kin*PD -kout*effect ## cp = center / v cp ~ prop(prop.err) + add(pkadd.err) effect ~ add(pdadd.err) | pca }) } fit <- nlmixr(pk.turnover.emax3, nlmixr2data::warfarin, \"monolix\", monolixControl(modelName=\"pk.turnover.emax3\")) #> ℹ assuming monolix is running because 'pk.turnover.emax3-monolix.txt' is present #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → finding duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling EBE model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → Calculating residuals/tables #> ✔ done #> → compress origData in nlmixr2 object, save 27560 #> ℹ monolix parameter history needs expoted charts, please export charts fit #> ── nlmixr² monolix ver 2021R1 ── #> #> OBJF AIC BIC Log-likelihood Condition#(Cov) #> monolix 1522.704 2448.398 2527.819 -1205.199 2203.836 #> Condition#(Cor) #> monolix 2.697324 #> #> ── Time (sec fit$time): ── #> #> setup table compress other #> elapsed 0.003452 0.084 0.01 5.166548 #> #> ── Population Parameters (fit$parFixed or fit$parFixedDf): ── #> #> Est. SE %RSE Back-transformed(95%CI) BSV(CV% or SD) #> tktr 0.218 0.179 82 1.24 (0.876, 1.77) 84.0 #> tka 0.00533 0.117 2.19e+03 1.01 (0.8, 1.26) 48.6 #> tcl -2.01 0.0518 2.58 0.135 (0.122, 0.149) 28.5 #> tv 2.04 0.0438 2.14 7.73 (7.09, 8.42) 22.6 #> prop.err 0.0986 0.0986 #> pkadd.err 0.533 0.533 #> temax 4.46 0.527 11.8 0.989 (0.969, 0.996) 0.380 #> tec50 0.0786 0.0889 113 1.08 (0.909, 1.29) 47.8 #> tkout -2.94 0.0261 0.888 0.053 (0.0503, 0.0558) 7.87 #> te0 4.57 0.0114 0.249 96.7 (94.5, 98.9) 5.08 #> pdadd.err 3.79 3.79 #> Shrink(SD)% #> tktr 47.9% #> tka 48.9% #> tcl 1.25% #> tv 6.09% #> prop.err #> pkadd.err #> temax 91.9% #> tec50 6.29% #> tkout 36.6% #> te0 19.9% #> pdadd.err #> #> Covariance Type (fit$covMethod): MonolixLin #> No correlations in between subject variability (BSV) matrix #> Full BSV covariance (fit$omega) or correlation (fit$omegaR; diagonals=SDs) #> Distribution stats (mean/skewness/kurtosis/p-value) available in fit$shrink #> Censoring (fit$censInformation): No censoring #> Minimization message (fit$message): #> IPRED relative difference compared to Monolix IPRED: 0.09%; 95% percentile: (0.01%,0.49%); rtol=0.000941 #> PRED relative difference compared to Monolix PRED: 0.04%; 95% percentile: (0%,0.2%); rtol=0.000428 #> IPRED absolute difference compared to Monolix IPRED: atol=0.00911; 95% percentile: (0.000493, 0.0928) #> PRED absolute difference compared to Monolix PRED: atol=0.000428; 95% percentile: (3.14e-07, 0.203) #> monolix model: 'pk.turnover.emax3-monolix.mlxtran' #> #> ── Fit Data (object fit is a modified tibble): ── #> # A tibble: 483 × 35 #> ID TIME CMT DV PRED RES IPRED IRES IWRES eta.ktr eta.ka eta.cl #> #> 1 1 0.5 cp 0 1.40 -1.40 0.500 -0.500 -0.934 -0.638 -0.447 0.689 #> 2 1 1 cp 1.9 3.94 -2.04 1.62 0.284 0.511 -0.638 -0.447 0.689 #> 3 1 2 cp 3.3 8.30 -5.00 4.29 -0.987 -1.45 -0.638 -0.447 0.689 #> # ℹ 480 more rows #> # ℹ 23 more variables: eta.v , eta.emax , eta.ec50 , #> # eta.kout , eta.e0 , cp , depot , gut , #> # center , effect , ktr , ka , cl , v , #> # emax , ec50 , kout , e0 , DCP , PD , #> # kin , tad , dosenum "},{"path":"/articles/running-monlix.html","id":"optional-step-2-add-conditional-weighted-residualsfocei-objf-to-monolix","dir":"Articles","previous_headings":"","what":"Optional Step 2: Add conditional weighted residuals/focei objf to Monolix","title":"Running Monolix","text":"case NONMEM, gives things available Monolix, like adding conditional weighted residuals: add nlmixr’s CWRES well adding nlmixr2 FOCEi objective function now objective function compared based assumptions, compare performance Monolix NONMEM based objective function. fair, objective function values must always used caution. model performs predicts data far valuable.","code":"fit <- addCwres(fit) #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → calculate jacobian #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate sensitivities #> [====|====|====|====|====|====|====|====|====|====] 0:00:01 #> → calculate ∂(f)/∂(η) #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate ∂(R²)/∂(η) #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → finding duplicate expressions in inner model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in inner model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → finding duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling inner model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → finding duplicate expressions in FD model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in FD model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling EBE model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → compiling events FD model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → Calculating residuals/tables #> ✔ done"},{"path":"/articles/running-monlix.html","id":"optional-step-3-use-nlmixr2-for-vpc-reporting-etc-","dir":"Articles","previous_headings":"","what":"Optional Step 3: Use nlmixr2 for vpc, reporting, etc.","title":"Running Monolix","text":"Also since nlmixr2 object easy perform VPC :","code":"v1s <- vpcPlot(fit, show=list(obs_dv=TRUE), scales=\"free_y\") + ylab(\"Warfarin Cp [mg/L] or PCA\") + xlab(\"Time [h]\") #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ v2s <- vpcPlot(fit, show=list(obs_dv=TRUE), pred_corr = TRUE, scales=\"free_y\") + ylab(\"Prediction Corrected Warfarin Cp [mg/L] or PCA\") + xlab(\"Time [h]\") v1s v2s"},{"path":"/articles/running-monlix.html","id":"notes-about-monlix-data-translation","dir":"Articles","previous_headings":"","what":"Notes about Monlix data translation","title":"Running Monolix","text":"input dataset expected compatible rxode2 nlmixr2. dataset converted Monolix format: combination CMT Dose type creates unique ADM variable. ADM definition saved monolix model file babelmixr2 creates macro describing compartment, ie compartment(cmt=#, amount=stateName) babelmixr2 also creates macro type dosing: Bolus/infusion uses depot() adds modeled lag time (Tlag) bioavailibilty (p) specified Modeled rate uses depot() Tk0=amtDose/rate. babelmixr2 also adds modeled lag time (Tlag) bioavailibilty (p) specified Modeled duration uses depot() Tk0=dur, also add adds modeled lag time (Tlag) bioavailibilty (p) specified Turning compartment uses empty macro","code":""},{"path":"/articles/running-nonmem.html","id":"step-0-what-do-you-need-to-do-to-have-nlmixr2-run-nonmem-from-a-nlmixr2-model","dir":"Articles","previous_headings":"","what":"Step 0: What do you need to do to have nlmixr2 run NONMEM from a nlmixr2 model","title":"Running NONMEM with nlmixr2","text":"use NONMEM nlmixr, need change data nlmixr2 dataset. babelmixr2 heavy lifting . need setup run NONMEM. many cases easy; simply figure command run NONMEM (often useful use full command path). can set options(\"babelmixr2.nonmem\"=\"nmfe743\") use nonmemControl(runCommand=\"nmfe743\"). prefer options() method since need set . also function prefer (cover using function ).","code":""},{"path":"/articles/running-nonmem.html","id":"step-1-run-a-nlmixr2-in-nonmem","dir":"Articles","previous_headings":"","what":"Step 1: Run a nlmixr2 in NONMEM","title":"Running NONMEM with nlmixr2","text":"Lets take classic warfarin example start comparison. model use nlmixr2 vignettes : Now can run nlmixr2 model using NONMEM simply can run directly: way run ordinary nlmixr2 model, simply new estimation method \"nonmem\" new controller (nonmemControl()) setup options estimation. options nonmemControl() modelName helps control output directory NONMEM (specified babelmixr2 tries guess based model name based input). try , see NONMEM fails rounding errors. standard approach changing sigdig, sigl, tol etc, get successful NONMEM model convergence, course supported. babelmixr2 can .","code":"library(babelmixr2) pk.turnover.emax3 <- function() { ini({ tktr <- log(1) tka <- log(1) tcl <- log(0.1) tv <- log(10) ## eta.ktr ~ 1 eta.ka ~ 1 eta.cl ~ 2 eta.v ~ 1 prop.err <- 0.1 pkadd.err <- 0.1 ## temax <- logit(0.8) tec50 <- log(0.5) tkout <- log(0.05) te0 <- log(100) ## eta.emax ~ .5 eta.ec50 ~ .5 eta.kout ~ .5 eta.e0 ~ .5 ## pdadd.err <- 10 }) model({ ktr <- exp(tktr + eta.ktr) ka <- exp(tka + eta.ka) cl <- exp(tcl + eta.cl) v <- exp(tv + eta.v) emax = expit(temax+eta.emax) ec50 = exp(tec50 + eta.ec50) kout = exp(tkout + eta.kout) e0 = exp(te0 + eta.e0) ## DCP = center/v PD=1-emax*DCP/(ec50+DCP) ## effect(0) = e0 kin = e0*kout ## d/dt(depot) = -ktr * depot d/dt(gut) = ktr * depot -ka * gut d/dt(center) = ka * gut - cl / v * center d/dt(effect) = kin*PD -kout*effect ## cp = center / v cp ~ prop(prop.err) + add(pkadd.err) effect ~ add(pdadd.err) | pca }) } try(nlmixr(pk.turnover.emax3, nlmixr2data::warfarin, \"nonmem\", nonmemControl(readRounding=FALSE, modelName=\"pk.turnover.emax3\")), silent=TRUE) #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> #> #> WARNINGS AND ERRORS (IF ANY) FOR PROBLEM 1 #> #> (WARNING 2) NM-TRAN INFERS THAT THE DATA ARE POPULATION. #> #> #> 0MINIMIZATION TERMINATED #> DUE TO ROUNDING ERRORS (ERROR=134) #> NO. OF FUNCTION EVALUATIONS USED: 1088 #> NO. OF SIG. DIGITS UNREPORTABLE #> 0PARAMETER ESTIMATE IS NEAR ITS BOUNDARY #> #> nonmem model: 'pk.turnover.emax3-nonmem/pk.turnover.emax3.nmctl' #> → terminated with rounding errors, can force nlmixr2/rxode2 to read with nonmemControl(readRounding=TRUE) #> Error : nonmem minimization not successful"},{"path":"/articles/running-nonmem.html","id":"optional-step-2-recover-a-failed-nonmem-run","dir":"Articles","previous_headings":"","what":"Optional Step 2: Recover a failed NONMEM run","title":"Running NONMEM with nlmixr2","text":"One approaches ignore rounding errors occurred read nlmixr2 anyway: may see work happening expected need already completed model. reading NONMEM model, babelmixr2 grabs: NONMEM’s objective function value NONMEM’s covariance (available) NONMEM’s optimization history NONMEM’s final parameter estimates (including ETAs) NONMEM’s PRED IPRED values (validation purposes) used solve ODEs came nlmixr2 optimization procedure. means can compare IPRED PRED values nlmixr2/rxode2 know immediately model validates. similar procedure Kyle Baron advocates validating NONMEM model mrgsolve model (see https://mrgsolve.org/blog/posts/2022-05-validate-translation/ https://mrgsolve.org/blog/posts/2023-update-validation.html), advantage method need simply write one model get validated roxde2/nlmixr2 model. case can see validation print fit object: shows preds ipreds match NONMEM nlmixr2 quite well.","code":"# Can still load the model to get information (possibly pipe) and create a new model f <- nlmixr(pk.turnover.emax3, nlmixr2data::warfarin, \"nonmem\", nonmemControl(readRounding=TRUE, modelName=\"pk.turnover.emax3\")) #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → finding duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling EBE model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> rxode2 2.0.13.9000 using 1 threads (see ?getRxThreads) #> no cache: create with `rxCreateCache()` #> → Calculating residuals/tables #> ✔ done #> → compress origData in nlmixr2 object, save 27560 #> → compress parHistData in nlmixr2 object, save 5536 print(f) #> ── nlmixr² nonmem ver 7.4.3 ── #> #> OBJF AIC BIC Log-likelihood Condition#(Cov) #> nonmem focei 1326.91 2252.605 2332.025 -1107.302 NA #> Condition#(Cor) #> nonmem focei NA #> #> ── Time (sec $time): ── #> #> setup table compress NONMEM #> elapsed 0.029967 0.056 0.017 320.27 #> #> ── Population Parameters ($parFixed or $parFixedDf): ── #> #> Est. Back-transformed BSV(CV% or SD) Shrink(SD)% #> tktr 6.24e-07 1 86.5 59.8% #> tka -3.01e-06 1 86.5 59.8% #> tcl -2 0.135 28.6 1.34% #> tv 2.05 7.78 22.8 6.44% #> prop.err 0.0986 0.0986 #> pkadd.err 0.512 0.512 #> temax 6.42 0.998 0.00707 100.% #> tec50 0.141 1.15 45.0 6.06% #> tkout -2.95 0.0522 9.16 32.4% #> te0 4.57 96.6 5.24 18.1% #> pdadd.err 3.72 3.72 #> #> No correlations in between subject variability (BSV) matrix #> Full BSV covariance ($omega) or correlation ($omegaR; diagonals=SDs) #> Distribution stats (mean/skewness/kurtosis/p-value) available in $shrink #> Information about run found ($runInfo): #> • NONMEM terminated due to rounding errors, but reading into nlmixr2/rxode2 anyway #> Censoring ($censInformation): No censoring #> Minimization message ($message): #> #> #> WARNINGS AND ERRORS (IF ANY) FOR PROBLEM 1 #> #> (WARNING 2) NM-TRAN INFERS THAT THE DATA ARE POPULATION. #> #> #> 0MINIMIZATION TERMINATED #> DUE TO ROUNDING ERRORS (ERROR=134) #> NO. OF FUNCTION EVALUATIONS USED: 1088 #> NO. OF SIG. DIGITS UNREPORTABLE #> 0PARAMETER ESTIMATE IS NEAR ITS BOUNDARY #> #> IPRED relative difference compared to Nonmem IPRED: 0%; 95% percentile: (0%,0%); rtol=6.36e-06 #> PRED relative difference compared to Nonmem PRED: 0%; 95% percentile: (0%,0%); rtol=6.08e-06 #> IPRED absolute difference compared to Nonmem IPRED: 95% percentile: (2.53e-06, 0.000502); atol=7.15e-05 #> PRED absolute difference compared to Nonmem PRED: 95% percentile: (3.79e-07,0.00321); atol=6.08e-06 #> there are solving errors during optimization (see '$prderr') #> nonmem model: 'pk.turnover.emax3-nonmem/pk.turnover.emax3.nmctl' #> #> ── Fit Data (object is a modified tibble): ── #> # A tibble: 483 × 35 #> ID TIME CMT DV PRED RES IPRED IRES IWRES eta.ktr eta.ka eta.cl #> #> 1 1 0.5 cp 0 1.16 -1.16 0.444 -0.444 -0.864 -0.506 -0.506 0.699 #> 2 1 1 cp 1.9 3.37 -1.47 1.45 0.446 0.840 -0.506 -0.506 0.699 #> 3 1 2 cp 3.3 7.51 -4.21 3.96 -0.660 -1.03 -0.506 -0.506 0.699 #> # ℹ 480 more rows #> # ℹ 23 more variables: eta.v , eta.emax , eta.ec50 , #> # eta.kout , eta.e0 , cp , depot , gut , #> # center , effect , ktr , ka , cl , v , #> # emax , ec50 , kout , e0 , DCP , PD , #> # kin , tad , dosenum "},{"path":"/articles/running-nonmem.html","id":"optional-step-3-use-nlmixr2-to-help-understand-why-nonmem-failed","dir":"Articles","previous_headings":"","what":"Optional Step 3: Use nlmixr2 to help understand why NONMEM failed","title":"Running NONMEM with nlmixr2","text":"Since nlmixr2 fit, can interesting things fit couldn’t NONMEM even another translator. example, wanted add covariance step can getVarCov(): nlmixr2 generous constitutes covariance step. r,s covariance matrix “” successful covariance step focei, system fall back methods necessary. covariance matrix r,s, regarded caution, can still give us clues things working NONMEM. examining fit, can see shrinkage high temax, tktr tka, dropped, making things likely converge NONMEM.","code":"getVarCov(f) #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → calculate jacobian #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate sensitivities #> [====|====|====|====|====|====|====|====|====|====] 0:00:01 #> → calculate ∂(f)/∂(η) #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → finding duplicate expressions in inner model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in inner model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → finding duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling inner model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → finding duplicate expressions in FD model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in FD model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling EBE model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → compiling events FD model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> calculating covariance matrix #> [====|====|====|====|====|====|====|====|====|====] 0:00:09 #> Warning in foceiFitCpp_(.ret): using R matrix to calculate covariance, can #> check sandwich or S matrix with $covRS and $covS #> Warning in foceiFitCpp_(.ret): gradient problems with covariance; see #> $scaleInfo #> → compress origData in nlmixr2 object, save 27560 #> Updated original fit object f #> tktr tka tcl tv temax #> tktr 1.821078e-02 -1.512272e-02 -2.550343e-05 3.216116e-04 0.0015410335 #> tka -1.512272e-02 1.815814e-02 -1.992622e-05 3.175474e-04 0.0010345827 #> tcl -2.550343e-05 -1.992622e-05 2.477225e-04 1.181659e-05 -0.0008009162 #> tv 3.216116e-04 3.175474e-04 1.181659e-05 3.184497e-04 0.0010914727 #> temax 1.541033e-03 1.034583e-03 -8.009162e-04 1.091473e-03 7.5815740647 #> tec50 1.410716e-04 1.273505e-04 -3.578298e-04 1.229707e-04 0.0483191718 #> tkout 1.023011e-04 1.011022e-04 -9.757882e-05 1.188260e-04 -0.0189641465 #> te0 1.310259e-05 1.399880e-05 -9.833068e-06 1.232683e-05 -0.0004365713 #> tec50 tkout te0 #> tktr 0.0001410716 1.023011e-04 1.310259e-05 #> tka 0.0001273505 1.011022e-04 1.399880e-05 #> tcl -0.0003578298 -9.757882e-05 -9.833068e-06 #> tv 0.0001229707 1.188260e-04 1.232683e-05 #> temax 0.0483191718 -1.896415e-02 -4.365713e-04 #> tec50 0.0018345990 1.544065e-04 -1.357629e-04 #> tkout 0.0001544065 6.320302e-04 5.220487e-05 #> te0 -0.0001357629 5.220487e-05 8.843897e-05"},{"path":"/articles/running-nonmem.html","id":"optional-step-4-use-model-piping-to-get-a-successful-nonmem-run","dir":"Articles","previous_headings":"","what":"Optional Step 4: Use model piping to get a successful NONMEM run","title":"Running NONMEM with nlmixr2","text":"use model piping remove parameters, new run start last model’s best estimates (saving bunch model development time). case, specify output directory pk.turnover.emax4 control get following: can see NONMEM run now successful validates rxode2 model : One thing emphasize: unlike translators, know immediately translation model validate. Hence can start process confidence - know immediately something wrong. related converting NONMEM nlmixr2 fit. Since nlmixr2 object easy perform VPC (true NONMEM models):","code":"f2 <- f %>% model(ktr <- exp(tktr)) %>% model(ka <- exp(tka)) %>% model(emax = expit(temax)) %>% nlmixr(data=nlmixr2data::warfarin, est=\"nonmem\", control=nonmemControl(readRounding=FALSE, modelName=\"pk.turnover.emax4\")) #> ! remove between subject variability `eta.ktr` #> ! remove between subject variability `eta.ka` #> ! remove between subject variability `eta.emax` #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → finding duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|==== #> ====|====] 0:00:00 #> → optimizing duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling EBE model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → Calculating residuals/tables #> ✔ done #> → compress origData in nlmixr2 object, save 27560 #> → compress parHistData in nlmixr2 object, save 8800 f2 #> ── nlmixr² nonmem ver 7.4.3 ── #> #> OBJF AIC BIC Log-likelihood Condition#(Cov) #> nonmem focei 1418.923 2338.618 2405.498 -1153.309 1.852796e+16 #> Condition#(Cor) #> nonmem focei 18934770 #> #> ── Time (sec f2$time): ── #> #> setup table compress NONMEM #> elapsed 0.004675 0.055 0.017 505.59 #> #> ── Population Parameters (f2$parFixed or f2$parFixedDf): ── #> #> Est. SE %RSE Back-transformed(95%CI) BSV(CV%) #> tktr 6.24e-07 9.05e-05 1.45e+04 1 (1, 1) #> tka -3.57e-06 0.000153 4.29e+03 1 (1, 1) #> tcl -1.99 0.0639 3.2 0.136 (0.12, 0.154) 27.6 #> tv 2.05 2.66 130 7.76 (0.042, 1.44e+03) 23.6 #> prop.err 0.161 0.161 #> pkadd.err 0.571 0.571 #> temax 9.98 4.96 49.7 1 (0.565, 1) #> tec50 0.131 1.61 1.23e+03 1.14 (0.0489, 26.6) 43.6 #> tkout -2.96 28.3 954 0.0517 (4.63e-26, 5.77e+22) 8.63 #> te0 4.57 0.411 9 96.7 (43.2, 217) 5.19 #> pdadd.err 3.59 3.59 #> Shrink(SD)% #> tktr #> tka #> tcl 3.19% #> tv 10.7% #> prop.err #> pkadd.err #> temax #> tec50 7.12% #> tkout 33.8% #> te0 17.2% #> pdadd.err #> #> Covariance Type (f2$covMethod): nonmem.r,s #> No correlations in between subject variability (BSV) matrix #> Full BSV covariance (f2$omega) or correlation (f2$omegaR; diagonals=SDs) #> Distribution stats (mean/skewness/kurtosis/p-value) available in f2$shrink #> Censoring (f2$censInformation): No censoring #> Minimization message (f2$message): #> #> #> WARNINGS AND ERRORS (IF ANY) FOR PROBLEM 1 #> #> (WARNING 2) NM-TRAN INFERS THAT THE DATA ARE POPULATION. #> #> #> 0MINIMIZATION SUCCESSFUL #> HOWEVER, PROBLEMS OCCURRED WITH THE MINIMIZATION. #> REGARD THE RESULTS OF THE ESTIMATION STEP CAREFULLY, AND ACCEPT THEM ONLY #> AFTER CHECKING THAT THE COVARIANCE STEP PRODUCES REASONABLE OUTPUT. #> NO. OF FUNCTION EVALUATIONS USED: 2391 #> NO. OF SIG. DIGITS IN FINAL EST.: 4.1 #> #> IPRED relative difference compared to Nonmem IPRED: 0%; 95% percentile: (0%,0%); rtol=6.85e-06 #> PRED relative difference compared to Nonmem PRED: 0%; 95% percentile: (0%,0%); rtol=6.45e-06 #> IPRED absolute difference compared to Nonmem IPRED: 95% percentile: (2.89e-06, 0.000506); atol=7.19e-05 #> PRED absolute difference compared to Nonmem PRED: 95% percentile: (5.14e-07,0.00318); atol=6.45e-06 #> nonmem model: 'pk.turnover.emax4-nonmem/pk.turnover.emax4.nmctl' #> #> ── Fit Data (object f2 is a modified tibble): ── #> # A tibble: 483 × 32 #> ID TIME CMT DV PRED RES IPRED IRES IWRES eta.cl eta.v eta.ec50 #> #> 1 1 0.5 cp 0 1.16 -1.16 0.920 -0.920 -1.56 0.689 0.228 0.160 #> 2 1 1 cp 1.9 3.38 -1.48 2.68 -0.780 -1.09 0.689 0.228 0.160 #> 3 1 2 cp 3.3 7.53 -4.23 5.94 -2.64 -2.36 0.689 0.228 0.160 #> # ℹ 480 more rows #> # ℹ 20 more variables: eta.kout , eta.e0 , cp , depot , #> # gut , center , effect , ktr , ka , cl , #> # v , emax , ec50 , kout , e0 , DCP , PD , #> # kin , tad , dosenum v1s <- vpcPlot(f2, show=list(obs_dv=TRUE), scales=\"free_y\") + ylab(\"Warfarin Cp [mg/L] or PCA\") + xlab(\"Time [h]\") #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ v2s <- vpcPlot(f2, show=list(obs_dv=TRUE), pred_corr = TRUE, scales=\"free_y\") + ylab(\"Prediction Corrected Warfarin Cp [mg/L] or PCA\") + xlab(\"Time [h]\") library() v1s v2s"},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Matthew Fidler. Author, maintainer. Bill Denney. Author. Nook Fulloption. Contributor. goldfish art","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Fidler M, Xiong Y, Schoemaker R, Wilkins J, Trame M, Hooijmaijers R, Post T, Wang W (2023). nlmixr: Nonlinear Mixed Effects Models Population Pharmacokinetics Pharmacodynamics. R package version 0.1.1.9000, https://CRAN.R-project.org/package=nlmixr. Fidler M, Wilkins J, Hooijmaijers R, Post T, Schoemaker R, Trame M, Xiong Y, Wang W (2019). “Nonlinear Mixed-Effects Model Development Simulation Using nlmixr Related R Open-Source Packages.” CPT: Pharmacometrics & Systems Pharmacology, 8(9), 621–633. https://doi.org/10.1002/psp4.12445. Schoemaker R, Fidler M, Laveille C, Wilkins J, Hooijmaijers R, Post T, Trame M, Xiong Y, Wang W (2019). “Performance SAEM FOCEI Algorithms Open-Source, Nonlinear Mixed Effect Modeling Tool nlmixr.” CPT: Pharmacometrics & Systems Pharmacology, 8(12), 923–930. https://doi.org/10.1002/psp4.12471.","code":"@Manual{, title = {{nlmixr}: Nonlinear Mixed Effects Models in Population Pharmacokinetics and Pharmacodynamics}, author = {Matthew Fidler and Yuan Xiong and Rik Schoemaker and Justin Wilkins and Mirjam Trame and Richard Hooijmaijers and Teun Post and Wenping Wang}, year = {2023}, note = {R package version 0.1.1.9000}, url = {https://CRAN.R-project.org/package=nlmixr}, } @Article{, title = {Nonlinear Mixed-Effects Model Development and Simulation Using nlmixr and Related R Open-Source Packages}, author = {Matthew Fidler and Justin Wilkins and Richard Hooijmaijers and Teun Post and Rik Schoemaker and Mirjam Trame and Yuan Xiong and Wenping Wang}, journal = {CPT: Pharmacometrics \\& Systems Pharmacology}, year = {2019}, volume = {8}, pages = {621--633}, number = {9}, month = {sep}, abstract = {nlmixr is a free and open-source R package for fitting nonlinear pharmacokinetic (PK), pharmacodynamic (PD), joint PK-PD, and quantitative systems pharmacology mixed-effects models. Currently, nlmixr is capable of fitting both traditional compartmental PK models as well as more complex models implemented using ordinary differential equations. We believe that, over time, it will become a capable, credible alternative to commercial software tools, such as NONMEM, Monolix, and Phoenix NLME.}, address = {Hoboken}, publisher = {John Wiley and Sons Inc.}, url = {https://doi.org/10.1002/psp4.12445}, } @Article{, title = {Performance of the SAEM and FOCEI Algorithms in the Open-Source, Nonlinear Mixed Effect Modeling Tool nlmixr}, author = {Rik Schoemaker and Matthew Fidler and Christian Laveille and Justin Wilkins and Richard Hooijmaijers and Teun Post and Mirjam Trame and Yuan Xiong and Wenping Wang}, journal = {CPT: Pharmacometrics \\& Systems Pharmacology}, year = {2019}, volume = {8}, pages = {923--930}, number = {12}, month = {dec}, abstract = {The free and open-source package nlmixr implements pharmacometric nonlinear mixed effects model parameter estimation in R. It provides a uniform language to define pharmacometric models using ordinary differential equations. Performances of the stochastic approximation expectation-maximization (SAEM) and first order-conditional estimation with interaction (FOCEI) algorithms in nlmixr were compared with those found in the industry standards, Monolix and NONMEM, using the following two scenarios: a simple model fit to 500 sparsely sampled data sets and a range of more complex compartmental models with linear and nonlinear clearance fit to data sets with rich sampling. Estimation results obtained from nlmixr for FOCEI and SAEM matched the corresponding output from NONMEM/FOCEI and Monolix/SAEM closely both in terms of parameter estimates and associated standard errors. These results indicate that nlmixr may provide a viable alternative to existing tools for pharmacometric parameter estimation.}, url = {https://doi.org/10.1002/psp4.12471}, }"},{"path":"/index.html","id":"babelmixr2","dir":"","previous_headings":"","what":"Use nlmixr2 to Interact with Open Source and Commercial Software","title":"Use nlmixr2 to Interact with Open Source and Commercial Software","text":"goal babelmixr2 convert nlmixr2 syntax commonly used tools.","code":""},{"path":"/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Use nlmixr2 to Interact with Open Source and Commercial Software","text":"can install released version babelmixr2 CRAN : can install r-universe : Otherwise can always install GitHub:","code":"install.packages(\"babelmixr2\") # Download and install babelmixr2 in R install.packages('babelmixr2', repos = c( nlmixr2 = 'https://nlmixr2.r-universe.dev', CRAN = 'https://cloud.r-project.org'))"},{"path":"/index.html","id":"what-you-can-do-with-babelmixr2","dir":"","previous_headings":"","what":"What you can do with babelmixr2","title":"Use nlmixr2 to Interact with Open Source and Commercial Software","text":"Babelmixr2 can help : Running nlmixr2 model commercial nonlinear mixed effects modeling tool like NONMEM Monolix Convert NONMEM model nlmixr2 model (conjunction nonmem2rx) Calculate scaling factors automatically add initial conditions based non-compartmental analysis (using PKNCA)","code":""},{"path":"/index.html","id":"monolix-setup","dir":"","previous_headings":"","what":"Monolix Setup","title":"Use nlmixr2 to Interact with Open Source and Commercial Software","text":"required, can get/install R ‘lixoftConnectors’ package ‘Monolix’ installation, described following url https://monolix.lixoft.com/monolix-api/lixoftconnectors_installation/. ‘lixoftConnectors’ available, R can run ‘Monolix’ directly instead using command line.","code":""},{"path":"/index.html","id":"example","dir":"","previous_headings":"","what":"Example","title":"Use nlmixr2 to Interact with Open Source and Commercial Software","text":"installed, use standard interface, can convert Monolix , can convert NONMEM ","code":"mod <- nlmixr(nlmixrFun, nlmmixrData, est=\"monolix\") mod <- nlmixr(nlmixrFun, nlmmixrData, est=\"nonmem\")"},{"path":"/reference/as.nlmixr2.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert an object to a nlmixr2 fit object — as.nlmixr2","title":"Convert an object to a nlmixr2 fit object — as.nlmixr2","text":"Convert object nlmixr2 fit object","code":""},{"path":"/reference/as.nlmixr2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert an object to a nlmixr2 fit object — as.nlmixr2","text":"","code":"as.nlmixr2( x, ..., table = nlmixr2est::tableControl(), rxControl = rxode2::rxControl() ) as.nlmixr( x, ..., table = nlmixr2est::tableControl(), rxControl = rxode2::rxControl() )"},{"path":"/reference/as.nlmixr2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert an object to a nlmixr2 fit object — as.nlmixr2","text":"x Object convert ... arguments table `nlmixr2est::tableControl()` options rxControl `rxode2::rxControl()` options, generally needed `addl` doses handled translation","code":""},{"path":"/reference/as.nlmixr2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert an object to a nlmixr2 fit object — as.nlmixr2","text":"nlmixr2 fit object","code":""},{"path":"/reference/as.nlmixr2.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convert an object to a nlmixr2 fit object — as.nlmixr2","text":"Matthew L. Fidler","code":""},{"path":"/reference/as.nlmixr2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert an object to a nlmixr2 fit object — as.nlmixr2","text":"","code":"# \\donttest{ # First read in the model (but without residuals) mod <- nonmem2rx(system.file(\"mods/cpt/runODE032.ctl\", package=\"nonmem2rx\"), determineError=FALSE, lst=\".res\", save=FALSE) #> ℹ getting information from '/home/runner/work/_temp/Library/nonmem2rx/mods/cpt/runODE032.ctl' #> ℹ reading in xml file #> ℹ done #> ℹ reading in phi file #> ℹ done #> ℹ reading in lst file #> ℹ abbreviated list parsing #> ℹ done #> ℹ done #> ℹ splitting control stream by records #> ℹ done #> ℹ Processing record $INPUT #> ℹ Processing record $MODEL #> ℹ Processing record $THETA #> ℹ Processing record $OMEGA #> ℹ Processing record $SIGMA #> ℹ Processing record $PROBLEM #> ℹ Processing record $DATA #> ℹ Processing record $SUBROUTINES #> ℹ Processing record $PK #> ℹ Processing record $DES #> ℹ Processing record $ERROR #> ℹ Processing record $ESTIMATION #> ℹ Ignore record $ESTIMATION #> ℹ Processing record $COVARIANCE #> ℹ Ignore record $COVARIANCE #> ℹ Processing record $TABLE #> ℹ change initial estimate of `theta1` to `1.37034036528946` #> ℹ change initial estimate of `theta2` to `4.19814911033061` #> ℹ change initial estimate of `theta3` to `1.38003493562413` #> ℹ change initial estimate of `theta4` to `3.87657341967489` #> ℹ change initial estimate of `theta5` to `0.196446108190896` #> ℹ change initial estimate of `eta1` to `0.101251418415006` #> ℹ change initial estimate of `eta2` to `0.0993872449483344` #> ℹ change initial estimate of `eta3` to `0.101302674763154` #> ℹ change initial estimate of `eta4` to `0.0730497519364148` #> ℹ read in nonmem input data (for model validation): /home/runner/work/_temp/Library/nonmem2rx/mods/cpt/Bolus_2CPT.csv #> ℹ ignoring lines that begin with a letter (IGNORE=@)' #> ℹ applying names specified by $INPUT #> ℹ subsetting accept/ignore filters code: .data[-which((.data$SD == 0)),] #> ℹ done #> #> #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ℹ read in nonmem IPRED data (for model validation): /home/runner/work/_temp/Library/nonmem2rx/mods/cpt/runODE032.csv #> ℹ done #> ℹ changing most variables to lower case #> ℹ done #> ℹ replace theta names #> ℹ done #> ℹ replace eta names #> ℹ done (no labels) #> ℹ renaming compartments #> ℹ done #> #> #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ℹ solving ipred problem #> ℹ done #> ℹ solving pred problem #> ℹ done # define the model with residuals (and change the name of the # parameters) In this step you need to be careful to not change the # estimates and make sure the residual estimates are correct (could # have to change var to sd). mod2 <-function() { ini({ lcl <- 1.37034036528946 lvc <- 4.19814911033061 lq <- 1.38003493562413 lvp <- 3.87657341967489 RSV <- c(0, 0.196446108190896, 1) eta.cl ~ 0.101251418415006 eta.v ~ 0.0993872449483344 eta.q ~ 0.101302674763154 eta.v2 ~ 0.0730497519364148 }) model({ cmt(CENTRAL) cmt(PERI) cl <- exp(lcl + eta.cl) v <- exp(lvc + eta.v) q <- exp(lq + eta.q) v2 <- exp(lvp + eta.v2) v1 <- v scale1 <- v k21 <- q/v2 k12 <- q/v d/dt(CENTRAL) <- k21 * PERI - k12 * CENTRAL - cl * CENTRAL/v1 d/dt(PERI) <- -k21 * PERI + k12 * CENTRAL f <- CENTRAL/scale1 f ~ prop(RSV) }) } # now we create another nonmem2rx object that validates the model above: new <- as.nonmem2rx(mod2, mod) #> #> #> #> #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ℹ solving ipred problem #> ℹ done #> ℹ solving pred problem #> ℹ done # once that is done, you can translate to a full nlmixr2 fit (if you wish) fit <- as.nlmixr2(new) #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → finding duplicate expressions in EBE model... #> → optimizing duplicate expressions in EBE model... #> → compiling EBE model... #> #> #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> rxode2 2.0.13.9000 using 1 threads (see ?getRxThreads) #> no cache: create with `rxCreateCache()` #> → Calculating residuals/tables #> ✔ done #> → compress origData in nlmixr2 object, save 204016 #> → compress parHistData in nlmixr2 object, save 2176 print(fit) #> ── nlmixr² nonmem2rx reading NONMEM ver 7.4.3 ── #> #> OBJF AIC BIC Log-likelihood Condition#(Cov) #> nonmem2rx 15977.28 20185.64 20237.23 -10083.82 335.4129 #> Condition#(Cor) #> nonmem2rx 2.096559 #> #> ── Time (sec $time): ── #> #> setup table compress NONMEM as.nlmixr2 #> elapsed 0.032108 0.076 0.025 100.95 3.559 #> #> ── Population Parameters ($parFixed or $parFixedDf): ── #> #> Est. SE %RSE Back-transformed(95%CI) BSV(CV%) Shrink(SD)% #> lcl 1.37 0.0298 2.17 3.94 (3.71, 4.17) 32.6 1.94% #> lvc 4.2 0.0295 0.703 66.6 (62.8, 70.5) 32.3 2.46% #> lq 1.38 0.0547 3.96 3.98 (3.57, 4.42) 32.7 40.5% #> lvp 3.88 0.0348 0.899 48.3 (45.1, 51.7) 27.5 28.4% #> RSV 0.196 0.196 #> #> Covariance Type ($covMethod): nonmem2rx #> No correlations in between subject variability (BSV) matrix #> Full BSV covariance ($omega) or correlation ($omegaR; diagonals=SDs) #> Distribution stats (mean/skewness/kurtosis/p-value) available in $shrink #> Censoring ($censInformation): No censoring #> Minimization message ($message): #> #> #> WARNINGS AND ERRORS (IF ANY) FOR PROBLEM 1 #> #> (WARNING 2) NM-TRAN INFERS THAT THE DATA ARE POPULATION. #> #> #> 0MINIMIZATION SUCCESSFUL #> NO. OF FUNCTION EVALUATIONS USED: 320 #> NO. OF SIG. DIGITS IN FINAL EST.: 2.5 #> #> IPRED relative difference compared to Nonmem IPRED: 0%; 95% percentile: (0%,0%); rtol=6.43e-06 #> PRED relative difference compared to Nonmem PRED: 0%; 95% percentile: (0%,0%); rtol=6.41e-06 #> IPRED absolute difference compared to Nonmem IPRED: 95% percentile: (2.25e-05, 0.0418); atol=0.00167 #> PRED absolute difference compared to Nonmem PRED: 95% percentile: (1.41e-07,0.00382); atol=6.41e-06 #> nonmem2rx model file: '/home/runner/work/_temp/Library/nonmem2rx/mods/cpt/runODE032.ctl' #> #> ── Fit Data (object is a modified tibble): ── #> # A tibble: 2,280 × 25 #> ID TIME DV PRED RES IPRED IRES IWRES eta.cl eta.v eta.q eta.v2 #> #> 1 1 0.25 1041. 1750. -710. 1215. -175. -0.732 -0.144 0.375 0.0650 0.241 #> 2 1 0.5 1629 1700. -70.8 1192. 437. 1.87 -0.144 0.375 0.0650 0.241 #> 3 1 0.75 878. 1651. -774. 1169. -291. -1.27 -0.144 0.375 0.0650 0.241 #> # ℹ 2,277 more rows #> # ℹ 13 more variables: f , CENTRAL , PERI , cl , v , #> # q , v2 , v1 , scale1 , k21 , k12 , tad , #> # dosenum # }"},{"path":"/reference/bblDatToMonolix.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","title":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","text":"Convert nlmixr2-compatible data formats (possible)","code":""},{"path":"/reference/bblDatToMonolix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","text":"","code":"bblDatToMonolix( model, data, table = nlmixr2est::tableControl(), rxControl = rxode2::rxControl(), env = NULL ) bblDatToNonmem( model, data, table = nlmixr2est::tableControl(), rxControl = rxode2::rxControl(), env = NULL ) bblDatToRxode( model, data, table = nlmixr2est::tableControl(), rxControl = rxode2::rxControl(), env = NULL ) bblDatToMrgsolve( model, data, table = nlmixr2est::tableControl(), rxControl = rxode2::rxControl(), env = NULL ) bblDatToPknca( model, data, table = nlmixr2est::tableControl(), rxControl = rxode2::rxControl(), env = NULL )"},{"path":"/reference/bblDatToMonolix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","text":"model rxode2 model conversion data Input dataset. table table control; mostly figure additional columns keep. rxControl rxode2 control options; figure handle addl dosing information. env `NULL` (default) nothing done. environment, function `nlmixr2est::.foceiPreProcessData(data, env, model, rxControl)` called provided environment.","code":""},{"path":"/reference/bblDatToMonolix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","text":"function `bblDatToMonolix()` return list : - Monolix compatible dataset ($monolix) - Monolix ADM information ($adm) function `nlmixrDataToNonmem()` return dataset compatible NONMEM. function `nlmixrDataToMrgsolve()` return dataset compatible `mrgsolve`. Unlike NONMEM, supports replacement events `evid=8` (note `rxode2` replacement `evid` `5`). function `nlmixrDataToRxode()` normalize dataset use newer `evid` definitions closer NONMEM instead classic definitions used lower level","code":""},{"path":"/reference/bblDatToMonolix.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","text":"Matthew L. Fidler","code":""},{"path":"/reference/bblDatToMonolix.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","text":"","code":"pk.turnover.emax3 <- function() { ini({ tktr <- log(1) tka <- log(1) tcl <- log(0.1) tv <- log(10) ## eta.ktr ~ 1 eta.ka ~ 1 eta.cl ~ 2 eta.v ~ 1 prop.err <- 0.1 pkadd.err <- 0.1 ## temax <- logit(0.8) tec50 <- log(0.5) tkout <- log(0.05) te0 <- log(100) ## eta.emax ~ .5 eta.ec50 ~ .5 eta.kout ~ .5 eta.e0 ~ .5 ## pdadd.err <- 10 }) model({ ktr <- exp(tktr + eta.ktr) ka <- exp(tka + eta.ka) cl <- exp(tcl + eta.cl) v <- exp(tv + eta.v) emax = expit(temax+eta.emax) ec50 = exp(tec50 + eta.ec50) kout = exp(tkout + eta.kout) e0 = exp(te0 + eta.e0) ## DCP = center/v PD=1-emax*DCP/(ec50+DCP) ## effect(0) = e0 kin = e0*kout ## d/dt(depot) = -ktr * depot d/dt(gut) = ktr * depot -ka * gut d/dt(center) = ka * gut - cl / v * center d/dt(effect) = kin*PD -kout*effect ## cp = center / v cp ~ prop(prop.err) + add(pkadd.err) effect ~ add(pdadd.err) | pca }) } bblDatToMonolix(pk.turnover.emax3, nlmixr2data::warfarin) #> #> #> $monolix #> ID TIME EVID AMT II DV ADM YTYPE SS nlmixrRowNums #> 1 1 0.0 1 100.0 0 NA 1 0 0 1 #> 2 1 0.5 0 NA 0 0.0 0 1 0 2 #> 3 1 1.0 0 NA 0 1.9 0 1 0 3 #> 4 1 2.0 0 NA 0 3.3 0 1 0 4 #> 5 1 3.0 0 NA 0 6.6 0 1 0 5 #> 6 1 6.0 0 NA 0 9.1 0 1 0 6 #> 7 1 9.0 0 NA 0 10.8 0 1 0 7 #> 8 1 12.0 0 NA 0 8.6 0 1 0 8 #> 9 1 24.0 0 NA 0 5.6 0 1 0 9 #> 10 1 24.0 0 NA 0 44.0 0 2 0 10 #> 11 1 36.0 0 NA 0 4.0 0 1 0 11 #> 12 1 36.0 0 NA 0 27.0 0 2 0 12 #> 13 1 48.0 0 NA 0 2.7 0 1 0 13 #> 14 1 48.0 0 NA 0 28.0 0 2 0 14 #> 15 1 72.0 0 NA 0 0.8 0 1 0 15 #> 16 1 72.0 0 NA 0 31.0 0 2 0 16 #> 17 1 96.0 0 NA 0 60.0 0 2 0 17 #> 18 1 120.0 0 NA 0 65.0 0 2 0 18 #> 19 1 144.0 0 NA 0 71.0 0 2 0 19 #> 20 2 0.0 1 100.0 0 NA 1 0 0 20 #> 21 2 0.0 0 NA 0 100.0 0 2 0 21 #> 22 2 24.0 0 NA 0 9.2 0 1 0 22 #> 23 2 24.0 0 NA 0 49.0 0 2 0 23 #> 24 2 36.0 0 NA 0 8.5 0 1 0 24 #> 25 2 36.0 0 NA 0 32.0 0 2 0 25 #> 26 2 48.0 0 NA 0 6.4 0 1 0 26 #> 27 2 48.0 0 NA 0 26.0 0 2 0 27 #> 28 2 72.0 0 NA 0 4.8 0 1 0 28 #> 29 2 72.0 0 NA 0 22.0 0 2 0 29 #> 30 2 96.0 0 NA 0 3.1 0 1 0 30 #> 31 2 96.0 0 NA 0 28.0 0 2 0 31 #> 32 2 120.0 0 NA 0 2.5 0 1 0 32 #> 33 2 120.0 0 NA 0 33.0 0 2 0 33 #> 34 3 0.0 1 100.0 0 NA 1 0 0 34 #> 35 3 0.0 0 NA 0 100.0 0 2 0 35 #> 36 3 0.5 0 NA 0 0.0 0 1 0 36 #> 37 3 2.0 0 NA 0 8.4 0 1 0 37 #> 38 3 3.0 0 NA 0 9.7 0 1 0 38 #> 39 3 6.0 0 NA 0 9.8 0 1 0 39 #> 40 3 12.0 0 NA 0 11.0 0 1 0 40 #> 41 3 24.0 0 NA 0 8.3 0 1 0 41 #> 42 3 24.0 0 NA 0 46.0 0 2 0 42 #> 43 3 36.0 0 NA 0 7.7 0 1 0 43 #> 44 3 36.0 0 NA 0 22.0 0 2 0 44 #> 45 3 48.0 0 NA 0 6.3 0 1 0 45 #> 46 3 48.0 0 NA 0 19.0 0 2 0 46 #> 47 3 72.0 0 NA 0 4.1 0 1 0 47 #> 48 3 72.0 0 NA 0 20.0 0 2 0 48 #> 49 3 96.0 0 NA 0 3.0 0 1 0 49 #> 50 3 96.0 0 NA 0 42.0 0 2 0 50 #> 51 3 120.0 0 NA 0 1.4 0 1 0 51 #> 52 3 120.0 0 NA 0 49.0 0 2 0 52 #> 53 3 144.0 0 NA 0 54.0 0 2 0 53 #> 54 4 0.0 1 120.0 0 NA 1 0 0 54 #> 55 4 0.0 0 NA 0 100.0 0 2 0 55 #> 56 4 3.0 0 NA 0 12.0 0 1 0 56 #> 57 4 6.0 0 NA 0 13.2 0 1 0 57 #> 58 4 9.0 0 NA 0 14.4 0 1 0 58 #> 59 4 24.0 0 NA 0 9.6 0 1 0 59 #> 60 4 24.0 0 NA 0 30.0 0 2 0 60 #> 61 4 36.0 0 NA 0 8.2 0 1 0 61 #> 62 4 36.0 0 NA 0 24.0 0 2 0 62 #> 63 4 48.0 0 NA 0 7.8 0 1 0 63 #> 64 4 48.0 0 NA 0 13.0 0 2 0 64 #> 65 4 72.0 0 NA 0 5.8 0 1 0 65 #> 66 4 72.0 0 NA 0 9.0 0 2 0 66 #> 67 4 96.0 0 NA 0 4.3 0 1 0 67 #> 68 4 96.0 0 NA 0 9.0 0 2 0 68 #> 69 4 120.0 0 NA 0 3.0 0 1 0 69 #> 70 4 120.0 0 NA 0 11.0 0 2 0 70 #> 71 4 144.0 0 NA 0 12.0 0 2 0 71 #> 72 5 0.0 1 60.0 0 NA 1 0 0 72 #> 73 5 0.0 0 NA 0 82.0 0 2 0 73 #> 74 5 3.0 0 NA 0 11.1 0 1 0 74 #> 75 5 6.0 0 NA 0 11.9 0 1 0 75 #> 76 5 9.0 0 NA 0 9.8 0 1 0 76 #> 77 5 12.0 0 NA 0 11.0 0 1 0 77 #> 78 5 24.0 0 NA 0 8.5 0 1 0 78 #> 79 5 24.0 0 NA 0 43.0 0 2 0 79 #> 80 5 36.0 0 NA 0 7.6 0 1 0 80 #> 81 5 36.0 0 NA 0 25.0 0 2 0 81 #> 82 5 48.0 0 NA 0 5.4 0 1 0 82 #> 83 5 48.0 0 NA 0 18.0 0 2 0 83 #> 84 5 72.0 0 NA 0 4.5 0 1 0 84 #> 85 5 72.0 0 NA 0 17.0 0 2 0 85 #> 86 5 96.0 0 NA 0 3.3 0 1 0 86 #> 87 5 96.0 0 NA 0 23.0 0 2 0 87 #> 88 5 120.0 0 NA 0 2.3 0 1 0 88 #> 89 5 120.0 0 NA 0 29.0 0 2 0 89 #> 90 5 144.0 0 NA 0 41.0 0 2 0 90 #> 91 6 0.0 1 113.0 0 NA 1 0 0 91 #> 92 6 0.0 0 NA 0 100.0 0 2 0 92 #> 93 6 6.0 0 NA 0 8.6 0 1 0 93 #> 94 6 12.0 0 NA 0 8.6 0 1 0 94 #> 95 6 24.0 0 NA 0 7.0 0 1 0 95 #> 96 6 24.0 0 NA 0 34.0 0 2 0 96 #> 97 6 36.0 0 NA 0 5.7 0 1 0 97 #> 98 6 36.0 0 NA 0 23.0 0 2 0 98 #> 99 6 48.0 0 NA 0 4.7 0 1 0 99 #> 100 6 48.0 0 NA 0 20.0 0 2 0 100 #> 101 6 72.0 0 NA 0 3.3 0 1 0 101 #> 102 6 72.0 0 NA 0 16.0 0 2 0 102 #> 103 6 96.0 0 NA 0 2.3 0 1 0 103 #> 104 6 96.0 0 NA 0 17.0 0 2 0 104 #> 105 6 120.0 0 NA 0 1.7 0 1 0 105 #> 106 6 120.0 0 NA 0 18.0 0 2 0 106 #> 107 6 144.0 0 NA 0 25.0 0 2 0 107 #> 108 7 0.0 1 90.0 0 NA 1 0 0 108 #> 109 7 3.0 0 NA 0 13.4 0 1 0 109 #> 110 7 6.0 0 NA 0 12.4 0 1 0 110 #> 111 7 9.0 0 NA 0 12.7 0 1 0 111 #> 112 7 12.0 0 NA 0 8.8 0 1 0 112 #> 113 7 24.0 0 NA 0 6.1 0 1 0 113 #> 114 7 24.0 0 NA 0 36.0 0 2 0 114 #> 115 7 36.0 0 NA 0 3.5 0 1 0 115 #> 116 7 36.0 0 NA 0 33.0 0 2 0 116 #> 117 7 48.0 0 NA 0 1.8 0 1 0 117 #> 118 7 48.0 0 NA 0 28.0 0 2 0 118 #> 119 7 72.0 0 NA 0 1.5 0 1 0 119 #> 120 7 72.0 0 NA 0 52.0 0 2 0 120 #> 121 7 96.0 0 NA 0 1.0 0 1 0 121 #> 122 7 96.0 0 NA 0 80.0 0 2 0 122 #> 123 7 120.0 0 NA 0 90.0 0 2 0 123 #> 124 7 144.0 0 NA 0 100.0 0 2 0 124 #> 125 8 0.0 1 135.0 0 NA 1 0 0 125 #> 126 8 0.0 0 NA 0 88.0 0 2 0 126 #> 127 8 2.0 0 NA 0 17.6 0 1 0 127 #> 128 8 3.0 0 NA 0 17.3 0 1 0 128 #> 129 8 6.0 0 NA 0 15.0 0 1 0 129 #> 130 8 9.0 0 NA 0 15.0 0 1 0 130 #> 131 8 12.0 0 NA 0 12.4 0 1 0 131 #> 132 8 24.0 0 NA 0 7.9 0 1 0 132 #> 133 8 24.0 0 NA 0 35.0 0 2 0 133 #> 134 8 36.0 0 NA 0 7.9 0 1 0 134 #> 135 8 36.0 0 NA 0 20.0 0 2 0 135 #> 136 8 48.0 0 NA 0 5.1 0 1 0 136 #> 137 8 48.0 0 NA 0 12.0 0 2 0 137 #> 138 8 72.0 0 NA 0 3.6 0 1 0 138 #> 139 8 72.0 0 NA 0 16.0 0 2 0 139 #> 140 8 96.0 0 NA 0 2.4 0 1 0 140 #> 141 8 96.0 0 NA 0 23.0 0 2 0 141 #> 142 8 120.0 0 NA 0 2.0 0 1 0 142 #> 143 8 120.0 0 NA 0 36.0 0 2 0 143 #> 144 8 144.0 0 NA 0 48.0 0 2 0 144 #> 145 9 0.0 1 75.0 0 NA 1 0 0 145 #> 146 9 0.0 0 NA 0 92.0 0 2 0 146 #> 147 9 0.5 0 NA 0 0.0 0 1 0 147 #> 148 9 1.0 0 NA 0 1.0 0 1 0 148 #> 149 9 2.0 0 NA 0 4.6 0 1 0 149 #> 150 9 3.0 0 NA 0 12.7 0 1 0 150 #> 151 9 3.0 0 NA 0 8.0 0 1 0 151 #> 152 9 6.0 0 NA 0 12.7 0 1 0 152 #> 153 9 6.0 0 NA 0 11.5 0 1 0 153 #> 154 9 9.0 0 NA 0 12.9 0 1 0 154 #> 155 9 9.0 0 NA 0 11.4 0 1 0 155 #> 156 9 12.0 0 NA 0 11.4 0 1 0 156 #> 157 9 12.0 0 NA 0 11.0 0 1 0 157 #> 158 9 24.0 0 NA 0 9.1 0 1 0 158 #> 159 9 24.0 0 NA 0 33.0 0 2 0 159 #> 160 9 36.0 0 NA 0 8.2 0 1 0 160 #> 161 9 36.0 0 NA 0 22.0 0 2 0 161 #> 162 9 48.0 0 NA 0 5.9 0 1 0 162 #> 163 9 48.0 0 NA 0 16.0 0 2 0 163 #> 164 9 72.0 0 NA 0 3.6 0 1 0 164 #> 165 9 72.0 0 NA 0 18.0 0 2 0 165 #> 166 9 96.0 0 NA 0 1.7 0 1 0 166 #> 167 9 96.0 0 NA 0 32.0 0 2 0 167 #> 168 9 120.0 0 NA 0 1.1 0 1 0 168 #> 169 9 120.0 0 NA 0 30.0 0 2 0 169 #> 170 9 144.0 0 NA 0 45.0 0 2 0 170 #> 171 10 0.0 1 105.0 0 NA 1 0 0 171 #> 172 10 0.0 0 NA 0 90.0 0 2 0 172 #> 173 10 24.0 0 NA 0 8.6 0 1 0 173 #> 174 10 24.0 0 NA 0 39.0 0 2 0 174 #> 175 10 36.0 0 NA 0 8.0 0 1 0 175 #> 176 10 36.0 0 NA 0 22.0 0 2 0 176 #> 177 10 48.0 0 NA 0 6.0 0 1 0 177 #> 178 10 48.0 0 NA 0 17.0 0 2 0 178 #> 179 10 72.0 0 NA 0 4.4 0 1 0 179 #> 180 10 72.0 0 NA 0 17.0 0 2 0 180 #> 181 10 96.0 0 NA 0 3.6 0 1 0 181 #> 182 10 96.0 0 NA 0 22.0 0 2 0 182 #> 183 10 120.0 0 NA 0 2.8 0 1 0 183 #> 184 10 120.0 0 NA 0 25.0 0 2 0 184 #> 185 10 144.0 0 NA 0 33.0 0 2 0 185 #> 186 11 0.0 1 123.0 0 NA 1 0 0 186 #> 187 11 0.0 0 NA 0 100.0 0 2 0 187 #> 188 11 1.5 0 NA 0 11.4 0 1 0 188 #> 189 11 3.0 0 NA 0 15.4 0 1 0 189 #> 190 11 6.0 0 NA 0 17.5 0 1 0 190 #> 191 11 12.0 0 NA 0 14.0 0 1 0 191 #> 192 11 24.0 0 NA 0 9.0 0 1 0 192 #> 193 11 24.0 0 NA 0 37.0 0 2 0 193 #> 194 11 36.0 0 NA 0 8.9 0 1 0 194 #> 195 11 36.0 0 NA 0 24.0 0 2 0 195 #> 196 11 48.0 0 NA 0 6.6 0 1 0 196 #> 197 11 48.0 0 NA 0 14.0 0 2 0 197 #> 198 11 72.0 0 NA 0 4.2 0 1 0 198 #> 199 11 72.0 0 NA 0 11.0 0 2 0 199 #> 200 11 96.0 0 NA 0 3.6 0 1 0 200 #> 201 11 96.0 0 NA 0 14.0 0 2 0 201 #> 202 11 120.0 0 NA 0 2.6 0 1 0 202 #> 203 11 120.0 0 NA 0 23.0 0 2 0 203 #> 204 11 144.0 0 NA 0 33.0 0 2 0 204 #> 205 12 0.0 1 113.0 0 NA 1 0 0 205 #> 206 12 0.0 0 NA 0 85.0 0 2 0 206 #> 207 12 1.5 0 NA 0 0.6 0 1 0 207 #> 208 12 3.0 0 NA 0 2.8 0 1 0 208 #> 209 12 6.0 0 NA 0 13.8 0 1 0 209 #> 210 12 9.0 0 NA 0 15.0 0 1 0 210 #> 211 12 24.0 0 NA 0 10.5 0 1 0 211 #> 212 12 24.0 0 NA 0 25.0 0 2 0 212 #> 213 12 36.0 0 NA 0 9.1 0 1 0 213 #> 214 12 36.0 0 NA 0 15.0 0 2 0 214 #> 215 12 48.0 0 NA 0 6.6 0 1 0 215 #> 216 12 48.0 0 NA 0 11.0 0 2 0 216 #> 217 12 72.0 0 NA 0 4.9 0 1 0 217 #> 218 12 96.0 0 NA 0 2.4 0 1 0 218 #> 219 12 120.0 0 NA 0 1.9 0 1 0 219 #> 220 13 0.0 1 113.0 0 NA 1 0 0 220 #> 221 13 0.0 0 NA 0 88.0 0 2 0 221 #> 222 13 1.5 0 NA 0 3.6 0 1 0 222 #> 223 13 3.0 0 NA 0 12.9 0 1 0 223 #> 224 13 6.0 0 NA 0 12.9 0 1 0 224 #> 225 13 9.0 0 NA 0 10.2 0 1 0 225 #> 226 13 24.0 0 NA 0 6.4 0 1 0 226 #> 227 13 24.0 0 NA 0 41.0 0 2 0 227 #> 228 13 36.0 0 NA 0 6.9 0 1 0 228 #> 229 13 36.0 0 NA 0 23.0 0 2 0 229 #> 230 13 48.0 0 NA 0 4.5 0 1 0 230 #> 231 13 48.0 0 NA 0 16.0 0 2 0 231 #> 232 13 72.0 0 NA 0 3.2 0 1 0 232 #> 233 13 72.0 0 NA 0 14.0 0 2 0 233 #> 234 13 96.0 0 NA 0 2.4 0 1 0 234 #> 235 13 96.0 0 NA 0 18.0 0 2 0 235 #> 236 13 120.0 0 NA 0 1.3 0 1 0 236 #> 237 13 120.0 0 NA 0 22.0 0 2 0 237 #> 238 13 144.0 0 NA 0 35.0 0 2 0 238 #> 239 14 0.0 1 75.0 0 NA 1 0 0 239 #> 240 14 0.0 0 NA 0 85.0 0 2 0 240 #> 241 14 0.5 0 NA 0 0.0 0 1 0 241 #> 242 14 1.0 0 NA 0 2.7 0 1 0 242 #> 243 14 2.0 0 NA 0 11.6 0 1 0 243 #> 244 14 3.0 0 NA 0 11.6 0 1 0 244 #> 245 14 6.0 0 NA 0 11.3 0 1 0 245 #> 246 14 9.0 0 NA 0 9.7 0 1 0 246 #> 247 14 24.0 0 NA 0 6.5 0 1 0 247 #> 248 14 24.0 0 NA 0 32.0 0 2 0 248 #> 249 14 36.0 0 NA 0 5.2 0 1 0 249 #> 250 14 36.0 0 NA 0 22.0 0 2 0 250 #> 251 14 48.0 0 NA 0 3.6 0 1 0 251 #> 252 14 48.0 0 NA 0 21.0 0 2 0 252 #> 253 14 72.0 0 NA 0 2.4 0 1 0 253 #> 254 14 72.0 0 NA 0 28.0 0 2 0 254 #> 255 14 96.0 0 NA 0 0.9 0 1 0 255 #> 256 14 96.0 0 NA 0 38.0 0 2 0 256 #> 257 14 120.0 0 NA 0 46.0 0 2 0 257 #> 258 14 144.0 0 NA 0 65.0 0 2 0 258 #> 259 15 0.0 1 85.0 0 NA 1 0 0 259 #> 260 15 0.0 0 NA 0 100.0 0 2 0 260 #> 261 15 1.0 0 NA 0 6.6 0 1 0 261 #> 262 15 3.0 0 NA 0 11.9 0 1 0 262 #> 263 15 6.0 0 NA 0 11.7 0 1 0 263 #> 264 15 9.0 0 NA 0 12.2 0 1 0 264 #> 265 15 24.0 0 NA 0 8.1 0 1 0 265 #> 266 15 24.0 0 NA 0 43.0 0 2 0 266 #> 267 15 36.0 0 NA 0 7.4 0 1 0 267 #> 268 15 36.0 0 NA 0 26.0 0 2 0 268 #> 269 15 48.0 0 NA 0 6.8 0 1 0 269 #> 270 15 48.0 0 NA 0 15.0 0 2 0 270 #> 271 15 72.0 0 NA 0 5.3 0 1 0 271 #> 272 15 72.0 0 NA 0 13.0 0 2 0 272 #> 273 15 96.0 0 NA 0 3.0 0 1 0 273 #> 274 15 96.0 0 NA 0 21.0 0 2 0 274 #> 275 15 120.0 0 NA 0 2.0 0 1 0 275 #> 276 15 120.0 0 NA 0 28.0 0 2 0 276 #> 277 15 144.0 0 NA 0 39.0 0 2 0 277 #> 278 16 0.0 1 87.0 0 NA 1 0 0 278 #> 279 16 0.0 0 NA 0 100.0 0 2 0 279 #> 280 16 24.0 0 NA 0 10.4 0 1 0 280 #> 281 16 24.0 0 NA 0 42.0 0 2 0 281 #> 282 16 36.0 0 NA 0 8.9 0 1 0 282 #> 283 16 36.0 0 NA 0 32.0 0 2 0 283 #> 284 16 48.0 0 NA 0 7.0 0 1 0 284 #> 285 16 48.0 0 NA 0 26.0 0 2 0 285 #> 286 16 72.0 0 NA 0 4.4 0 1 0 286 #> 287 16 72.0 0 NA 0 31.0 0 2 0 287 #> 288 16 96.0 0 NA 0 3.2 0 1 0 288 #> 289 16 96.0 0 NA 0 33.0 0 2 0 289 #> 290 16 120.0 0 NA 0 2.4 0 1 0 290 #> 291 16 120.0 0 NA 0 54.0 0 2 0 291 #> 292 17 0.0 1 117.0 0 NA 1 0 0 292 #> 293 17 0.0 0 NA 0 100.0 0 2 0 293 #> 294 17 24.0 0 NA 0 7.6 0 1 0 294 #> 295 17 24.0 0 NA 0 35.0 0 2 0 295 #> 296 17 36.0 0 NA 0 6.4 0 1 0 296 #> 297 17 36.0 0 NA 0 23.0 0 2 0 297 #> 298 17 48.0 0 NA 0 6.0 0 1 0 298 #> 299 17 48.0 0 NA 0 17.0 0 2 0 299 #> 300 17 72.0 0 NA 0 4.0 0 1 0 300 #> 301 17 72.0 0 NA 0 18.0 0 2 0 301 #> 302 17 96.0 0 NA 0 3.1 0 1 0 302 #> 303 17 96.0 0 NA 0 18.0 0 2 0 303 #> 304 17 120.0 0 NA 0 2.0 0 1 0 304 #> 305 17 120.0 0 NA 0 21.0 0 2 0 305 #> 306 18 0.0 1 112.0 0 NA 1 0 0 306 #> 307 18 0.0 0 NA 0 100.0 0 2 0 307 #> 308 18 24.0 0 NA 0 7.6 0 1 0 308 #> 309 18 24.0 0 NA 0 32.0 0 2 0 309 #> 310 18 36.0 0 NA 0 6.6 0 1 0 310 #> 311 18 36.0 0 NA 0 20.0 0 2 0 311 #> 312 18 48.0 0 NA 0 5.4 0 1 0 312 #> 313 18 48.0 0 NA 0 18.0 0 2 0 313 #> 314 18 72.0 0 NA 0 3.4 0 1 0 314 #> 315 18 72.0 0 NA 0 18.0 0 2 0 315 #> 316 18 96.0 0 NA 0 1.2 0 1 0 316 #> 317 18 96.0 0 NA 0 19.0 0 2 0 317 #> 318 18 120.0 0 NA 0 0.9 0 1 0 318 #> 319 18 120.0 0 NA 0 29.0 0 2 0 319 #> 320 19 0.0 1 95.5 0 NA 1 0 0 320 #> 321 19 0.0 0 NA 0 100.0 0 2 0 321 #> 322 19 24.0 0 NA 0 6.6 0 1 0 322 #> 323 19 24.0 0 NA 0 33.0 0 2 0 323 #> 324 19 36.0 0 NA 0 5.3 0 1 0 324 #> 325 19 36.0 0 NA 0 28.0 0 2 0 325 #> 326 19 48.0 0 NA 0 3.6 0 1 0 326 #> 327 19 48.0 0 NA 0 18.0 0 2 0 327 #> 328 19 72.0 0 NA 0 2.7 0 1 0 328 #> 329 19 72.0 0 NA 0 18.0 0 2 0 329 #> 330 19 96.0 0 NA 0 1.4 0 1 0 330 #> 331 19 96.0 0 NA 0 17.0 0 2 0 331 #> 332 19 120.0 0 NA 0 1.1 0 1 0 332 #> 333 19 120.0 0 NA 0 26.0 0 2 0 333 #> 334 20 0.0 1 88.5 0 NA 1 0 0 334 #> 335 20 0.0 0 NA 0 100.0 0 2 0 335 #> 336 20 24.0 0 NA 0 9.6 0 1 0 336 #> 337 20 24.0 0 NA 0 41.0 0 2 0 337 #> 338 20 36.0 0 NA 0 8.0 0 1 0 338 #> 339 20 36.0 0 NA 0 30.0 0 2 0 339 #> 340 20 48.0 0 NA 0 6.6 0 1 0 340 #> 341 20 48.0 0 NA 0 22.0 0 2 0 341 #> 342 20 72.0 0 NA 0 5.6 0 1 0 342 #> 343 20 72.0 0 NA 0 23.0 0 2 0 343 #> 344 20 96.0 0 NA 0 3.5 0 1 0 344 #> 345 20 96.0 0 NA 0 23.0 0 2 0 345 #> 346 20 120.0 0 NA 0 2.3 0 1 0 346 #> 347 20 120.0 0 NA 0 35.0 0 2 0 347 #> 348 21 0.0 1 93.0 0 NA 1 0 0 348 #> 349 21 0.0 0 NA 0 100.0 0 2 0 349 #> 350 21 24.0 0 NA 0 7.3 0 1 0 350 #> 351 21 24.0 0 NA 0 46.0 0 2 0 351 #> 352 21 36.0 0 NA 0 6.1 0 1 0 352 #> 353 21 36.0 0 NA 0 27.0 0 2 0 353 #> 354 21 48.0 0 NA 0 4.3 0 1 0 354 #> 355 21 48.0 0 NA 0 22.0 0 2 0 355 #> 356 21 72.0 0 NA 0 3.2 0 1 0 356 #> 357 21 72.0 0 NA 0 36.0 0 2 0 357 #> 358 21 96.0 0 NA 0 2.3 0 1 0 358 #> 359 21 96.0 0 NA 0 40.0 0 2 0 359 #> 360 21 120.0 0 NA 0 1.9 0 1 0 360 #> 361 21 120.0 0 NA 0 44.0 0 2 0 361 #> 362 22 0.0 1 87.0 0 NA 1 0 0 362 #> 363 22 0.0 0 NA 0 100.0 0 2 0 363 #> 364 22 24.0 0 NA 0 8.9 0 1 0 364 #> 365 22 24.0 0 NA 0 35.0 0 2 0 365 #> 366 22 36.0 0 NA 0 8.4 0 1 0 366 #> 367 22 36.0 0 NA 0 27.0 0 2 0 367 #> 368 22 48.0 0 NA 0 8.0 0 1 0 368 #> 369 22 48.0 0 NA 0 23.0 0 2 0 369 #> 370 22 72.0 0 NA 0 4.4 0 1 0 370 #> 371 22 72.0 0 NA 0 27.0 0 2 0 371 #> 372 22 96.0 0 NA 0 3.2 0 1 0 372 #> 373 22 96.0 0 NA 0 43.0 0 2 0 373 #> 374 22 120.0 0 NA 0 1.7 0 1 0 374 #> 375 22 120.0 0 NA 0 43.0 0 2 0 375 #> 376 23 0.0 1 110.0 0 NA 1 0 0 376 #> 377 23 0.0 0 NA 0 100.0 0 2 0 377 #> 378 23 24.0 0 NA 0 9.8 0 1 0 378 #> 379 23 24.0 0 NA 0 34.0 0 2 0 379 #> 380 23 36.0 0 NA 0 8.4 0 1 0 380 #> 381 23 36.0 0 NA 0 24.0 0 2 0 381 #> 382 23 48.0 0 NA 0 6.6 0 1 0 382 #> 383 23 48.0 0 NA 0 15.0 0 2 0 383 #> 384 23 72.0 0 NA 0 4.8 0 1 0 384 #> 385 23 72.0 0 NA 0 15.0 0 2 0 385 #> 386 23 96.0 0 NA 0 3.2 0 1 0 386 #> 387 23 96.0 0 NA 0 19.0 0 2 0 387 #> 388 23 120.0 0 NA 0 2.4 0 1 0 388 #> 389 23 120.0 0 NA 0 19.0 0 2 0 389 #> 390 24 0.0 1 115.0 0 NA 1 0 0 390 #> 391 24 0.0 0 NA 0 88.0 0 2 0 391 #> 392 24 24.0 0 NA 0 8.2 0 1 0 392 #> 393 24 24.0 0 NA 0 37.0 0 2 0 393 #> 394 24 36.0 0 NA 0 7.5 0 1 0 394 #> 395 24 36.0 0 NA 0 20.0 0 2 0 395 #> 396 24 48.0 0 NA 0 6.8 0 1 0 396 #> 397 24 48.0 0 NA 0 20.0 0 2 0 397 #> 398 24 72.0 0 NA 0 5.5 0 1 0 398 #> 399 24 72.0 0 NA 0 26.0 0 2 0 399 #> 400 24 96.0 0 NA 0 4.5 0 1 0 400 #> 401 24 96.0 0 NA 0 28.0 0 2 0 401 #> 402 24 120.0 0 NA 0 3.7 0 1 0 402 #> 403 24 120.0 0 NA 0 50.0 0 2 0 403 #> 404 25 0.0 1 112.0 0 NA 1 0 0 404 #> 405 25 0.0 0 NA 0 100.0 0 2 0 405 #> 406 25 24.0 0 NA 0 11.0 0 1 0 406 #> 407 25 24.0 0 NA 0 32.0 0 2 0 407 #> 408 25 36.0 0 NA 0 10.0 0 1 0 408 #> 409 25 36.0 0 NA 0 20.0 0 2 0 409 #> 410 25 48.0 0 NA 0 8.2 0 1 0 410 #> 411 25 48.0 0 NA 0 17.0 0 2 0 411 #> 412 25 72.0 0 NA 0 6.0 0 1 0 412 #> 413 25 72.0 0 NA 0 19.0 0 2 0 413 #> 414 25 96.0 0 NA 0 3.7 0 1 0 414 #> 415 25 96.0 0 NA 0 21.0 0 2 0 415 #> 416 25 120.0 0 NA 0 2.6 0 1 0 416 #> 417 25 120.0 0 NA 0 30.0 0 2 0 417 #> 418 26 0.0 1 120.0 0 NA 1 0 0 418 #> 419 26 0.0 0 NA 0 100.0 0 2 0 419 #> 420 26 24.0 0 NA 0 10.0 0 1 0 420 #> 421 26 24.0 0 NA 0 41.0 0 2 0 421 #> 422 26 36.0 0 NA 0 9.0 0 1 0 422 #> 423 26 36.0 0 NA 0 28.0 0 2 0 423 #> 424 26 48.0 0 NA 0 7.3 0 1 0 424 #> 425 26 48.0 0 NA 0 19.0 0 2 0 425 #> 426 26 72.0 0 NA 0 5.2 0 1 0 426 #> 427 26 72.0 0 NA 0 17.0 0 2 0 427 #> 428 26 96.0 0 NA 0 3.7 0 1 0 428 #> 429 26 96.0 0 NA 0 17.0 0 2 0 429 #> 430 26 120.0 0 NA 0 2.7 0 1 0 430 #> 431 26 120.0 0 NA 0 24.0 0 2 0 431 #> 432 27 0.0 1 120.0 0 NA 1 0 0 432 #> 433 27 0.0 0 NA 0 100.0 0 2 0 433 #> 434 27 24.0 0 NA 0 11.8 0 1 0 434 #> 435 27 24.0 0 NA 0 32.0 0 2 0 435 #> 436 27 36.0 0 NA 0 9.2 0 1 0 436 #> 437 27 36.0 0 NA 0 21.0 0 2 0 437 #> 438 27 48.0 0 NA 0 7.7 0 1 0 438 #> 439 27 48.0 0 NA 0 19.0 0 2 0 439 #> 440 27 72.0 0 NA 0 4.9 0 1 0 440 #> 441 27 72.0 0 NA 0 22.0 0 2 0 441 #> 442 27 96.0 0 NA 0 3.4 0 1 0 442 #> 443 27 96.0 0 NA 0 33.0 0 2 0 443 #> 444 27 120.0 0 NA 0 2.7 0 1 0 444 #> 445 27 120.0 0 NA 0 46.0 0 2 0 445 #> 446 28 0.0 1 120.0 0 NA 1 0 0 446 #> 447 28 0.0 0 NA 0 100.0 0 2 0 447 #> 448 28 24.0 0 NA 0 10.1 0 1 0 448 #> 449 28 24.0 0 NA 0 39.0 0 2 0 449 #> 450 28 36.0 0 NA 0 8.0 0 1 0 450 #> 451 28 36.0 0 NA 0 25.0 0 2 0 451 #> 452 28 48.0 0 NA 0 6.0 0 1 0 452 #> 453 28 48.0 0 NA 0 16.0 0 2 0 453 #> 454 28 72.0 0 NA 0 4.9 0 1 0 454 #> 455 28 72.0 0 NA 0 14.0 0 2 0 455 #> 456 28 96.0 0 NA 0 3.4 0 1 0 456 #> 457 28 96.0 0 NA 0 15.0 0 2 0 457 #> 458 28 120.0 0 NA 0 2.0 0 1 0 458 #> 459 28 120.0 0 NA 0 20.0 0 2 0 459 #> 460 29 0.0 1 153.0 0 NA 1 0 0 460 #> 461 29 0.0 0 NA 0 86.0 0 2 0 461 #> 462 29 24.0 0 NA 0 8.3 0 1 0 462 #> 463 29 24.0 0 NA 0 35.0 0 2 0 463 #> 464 29 36.0 0 NA 0 7.0 0 1 0 464 #> 465 29 36.0 0 NA 0 21.0 0 2 0 465 #> 466 29 48.0 0 NA 0 5.6 0 1 0 466 #> 467 29 48.0 0 NA 0 18.0 0 2 0 467 #> 468 29 72.0 0 NA 0 4.1 0 1 0 468 #> 469 29 72.0 0 NA 0 20.0 0 2 0 469 #> 470 29 96.0 0 NA 0 3.1 0 1 0 470 #> 471 29 96.0 0 NA 0 29.0 0 2 0 471 #> 472 29 120.0 0 NA 0 2.2 0 1 0 472 #> 473 29 120.0 0 NA 0 41.0 0 2 0 473 #> 474 30 0.0 1 105.0 0 NA 1 0 0 474 #> 475 30 0.0 0 NA 0 100.0 0 2 0 475 #> 476 30 24.0 0 NA 0 9.9 0 1 0 476 #> 477 30 24.0 0 NA 0 45.0 0 2 0 477 #> 478 30 36.0 0 NA 0 7.5 0 1 0 478 #> 479 30 36.0 0 NA 0 24.0 0 2 0 479 #> 480 30 48.0 0 NA 0 6.5 0 1 0 480 #> 481 30 48.0 0 NA 0 23.0 0 2 0 481 #> 482 30 72.0 0 NA 0 4.1 0 1 0 482 #> 483 30 72.0 0 NA 0 26.0 0 2 0 483 #> 484 30 96.0 0 NA 0 2.9 0 1 0 484 #> 485 30 96.0 0 NA 0 28.0 0 2 0 485 #> 486 30 120.0 0 NA 0 2.3 0 1 0 486 #> 487 30 120.0 0 NA 0 39.0 0 2 0 487 #> 488 31 0.0 1 125.0 0 NA 1 0 0 488 #> 489 31 0.0 0 NA 0 100.0 0 2 0 489 #> 490 31 24.0 0 NA 0 9.5 0 1 0 490 #> 491 31 24.0 0 NA 0 45.0 0 2 0 491 #> 492 31 36.0 0 NA 0 7.8 0 1 0 492 #> 493 31 36.0 0 NA 0 30.0 0 2 0 493 #> 494 31 48.0 0 NA 0 6.4 0 1 0 494 #> 495 31 48.0 0 NA 0 24.0 0 2 0 495 #> 496 31 72.0 0 NA 0 4.5 0 1 0 496 #> 497 31 72.0 0 NA 0 22.0 0 2 0 497 #> 498 31 96.0 0 NA 0 3.4 0 1 0 498 #> 499 31 96.0 0 NA 0 28.0 0 2 0 499 #> 500 31 120.0 0 NA 0 2.5 0 1 0 500 #> 501 31 120.0 0 NA 0 42.0 0 2 0 501 #> 502 32 0.0 1 93.0 0 NA 1 0 0 502 #> 503 32 0.0 0 NA 0 100.0 0 2 0 503 #> 504 32 24.0 0 NA 0 8.9 0 1 0 504 #> 505 32 24.0 0 NA 0 36.0 0 2 0 505 #> 506 32 36.0 0 NA 0 7.7 0 1 0 506 #> 507 32 36.0 0 NA 0 27.0 0 2 0 507 #> 508 32 48.0 0 NA 0 6.9 0 1 0 508 #> 509 32 48.0 0 NA 0 24.0 0 2 0 509 #> 510 32 72.0 0 NA 0 4.4 0 1 0 510 #> 511 32 72.0 0 NA 0 23.0 0 2 0 511 #> 512 32 96.0 0 NA 0 3.5 0 1 0 512 #> 513 32 96.0 0 NA 0 20.0 0 2 0 513 #> 514 32 120.0 0 NA 0 2.5 0 1 0 514 #> 515 32 120.0 0 NA 0 22.0 0 2 0 515 #> #> $adm #> adm cmt type #> 1 1 1 bolus #> bblDatToNonmem(pk.turnover.emax3, nlmixr2data::warfarin) #> #> #> ID TIME EVID AMT DV CMT DVID nlmixrRowNums #> 1 1 0.0 1 100.0 NA 1 NA 1 #> 2 1 0.5 0 NA 0.0 NA 1 2 #> 3 1 1.0 0 NA 1.9 NA 1 3 #> 4 1 2.0 0 NA 3.3 NA 1 4 #> 5 1 3.0 0 NA 6.6 NA 1 5 #> 6 1 6.0 0 NA 9.1 NA 1 6 #> 7 1 9.0 0 NA 10.8 NA 1 7 #> 8 1 12.0 0 NA 8.6 NA 1 8 #> 9 1 24.0 0 NA 5.6 NA 1 9 #> 10 1 24.0 0 NA 44.0 NA 2 10 #> 11 1 36.0 0 NA 4.0 NA 1 11 #> 12 1 36.0 0 NA 27.0 NA 2 12 #> 13 1 48.0 0 NA 2.7 NA 1 13 #> 14 1 48.0 0 NA 28.0 NA 2 14 #> 15 1 72.0 0 NA 0.8 NA 1 15 #> 16 1 72.0 0 NA 31.0 NA 2 16 #> 17 1 96.0 0 NA 60.0 NA 2 17 #> 18 1 120.0 0 NA 65.0 NA 2 18 #> 19 1 144.0 0 NA 71.0 NA 2 19 #> 20 2 0.0 1 100.0 NA 1 NA 20 #> 21 2 0.0 0 NA 100.0 NA 2 21 #> 22 2 24.0 0 NA 9.2 NA 1 22 #> 23 2 24.0 0 NA 49.0 NA 2 23 #> 24 2 36.0 0 NA 8.5 NA 1 24 #> 25 2 36.0 0 NA 32.0 NA 2 25 #> 26 2 48.0 0 NA 6.4 NA 1 26 #> 27 2 48.0 0 NA 26.0 NA 2 27 #> 28 2 72.0 0 NA 4.8 NA 1 28 #> 29 2 72.0 0 NA 22.0 NA 2 29 #> 30 2 96.0 0 NA 3.1 NA 1 30 #> 31 2 96.0 0 NA 28.0 NA 2 31 #> 32 2 120.0 0 NA 2.5 NA 1 32 #> 33 2 120.0 0 NA 33.0 NA 2 33 #> 34 3 0.0 1 100.0 NA 1 NA 34 #> 35 3 0.0 0 NA 100.0 NA 2 35 #> 36 3 0.5 0 NA 0.0 NA 1 36 #> 37 3 2.0 0 NA 8.4 NA 1 37 #> 38 3 3.0 0 NA 9.7 NA 1 38 #> 39 3 6.0 0 NA 9.8 NA 1 39 #> 40 3 12.0 0 NA 11.0 NA 1 40 #> 41 3 24.0 0 NA 8.3 NA 1 41 #> 42 3 24.0 0 NA 46.0 NA 2 42 #> 43 3 36.0 0 NA 7.7 NA 1 43 #> 44 3 36.0 0 NA 22.0 NA 2 44 #> 45 3 48.0 0 NA 6.3 NA 1 45 #> 46 3 48.0 0 NA 19.0 NA 2 46 #> 47 3 72.0 0 NA 4.1 NA 1 47 #> 48 3 72.0 0 NA 20.0 NA 2 48 #> 49 3 96.0 0 NA 3.0 NA 1 49 #> 50 3 96.0 0 NA 42.0 NA 2 50 #> 51 3 120.0 0 NA 1.4 NA 1 51 #> 52 3 120.0 0 NA 49.0 NA 2 52 #> 53 3 144.0 0 NA 54.0 NA 2 53 #> 54 4 0.0 1 120.0 NA 1 NA 54 #> 55 4 0.0 0 NA 100.0 NA 2 55 #> 56 4 3.0 0 NA 12.0 NA 1 56 #> 57 4 6.0 0 NA 13.2 NA 1 57 #> 58 4 9.0 0 NA 14.4 NA 1 58 #> 59 4 24.0 0 NA 9.6 NA 1 59 #> 60 4 24.0 0 NA 30.0 NA 2 60 #> 61 4 36.0 0 NA 8.2 NA 1 61 #> 62 4 36.0 0 NA 24.0 NA 2 62 #> 63 4 48.0 0 NA 7.8 NA 1 63 #> 64 4 48.0 0 NA 13.0 NA 2 64 #> 65 4 72.0 0 NA 5.8 NA 1 65 #> 66 4 72.0 0 NA 9.0 NA 2 66 #> 67 4 96.0 0 NA 4.3 NA 1 67 #> 68 4 96.0 0 NA 9.0 NA 2 68 #> 69 4 120.0 0 NA 3.0 NA 1 69 #> 70 4 120.0 0 NA 11.0 NA 2 70 #> 71 4 144.0 0 NA 12.0 NA 2 71 #> 72 5 0.0 1 60.0 NA 1 NA 72 #> 73 5 0.0 0 NA 82.0 NA 2 73 #> 74 5 3.0 0 NA 11.1 NA 1 74 #> 75 5 6.0 0 NA 11.9 NA 1 75 #> 76 5 9.0 0 NA 9.8 NA 1 76 #> 77 5 12.0 0 NA 11.0 NA 1 77 #> 78 5 24.0 0 NA 8.5 NA 1 78 #> 79 5 24.0 0 NA 43.0 NA 2 79 #> 80 5 36.0 0 NA 7.6 NA 1 80 #> 81 5 36.0 0 NA 25.0 NA 2 81 #> 82 5 48.0 0 NA 5.4 NA 1 82 #> 83 5 48.0 0 NA 18.0 NA 2 83 #> 84 5 72.0 0 NA 4.5 NA 1 84 #> 85 5 72.0 0 NA 17.0 NA 2 85 #> 86 5 96.0 0 NA 3.3 NA 1 86 #> 87 5 96.0 0 NA 23.0 NA 2 87 #> 88 5 120.0 0 NA 2.3 NA 1 88 #> 89 5 120.0 0 NA 29.0 NA 2 89 #> 90 5 144.0 0 NA 41.0 NA 2 90 #> 91 6 0.0 1 113.0 NA 1 NA 91 #> 92 6 0.0 0 NA 100.0 NA 2 92 #> 93 6 6.0 0 NA 8.6 NA 1 93 #> 94 6 12.0 0 NA 8.6 NA 1 94 #> 95 6 24.0 0 NA 7.0 NA 1 95 #> 96 6 24.0 0 NA 34.0 NA 2 96 #> 97 6 36.0 0 NA 5.7 NA 1 97 #> 98 6 36.0 0 NA 23.0 NA 2 98 #> 99 6 48.0 0 NA 4.7 NA 1 99 #> 100 6 48.0 0 NA 20.0 NA 2 100 #> 101 6 72.0 0 NA 3.3 NA 1 101 #> 102 6 72.0 0 NA 16.0 NA 2 102 #> 103 6 96.0 0 NA 2.3 NA 1 103 #> 104 6 96.0 0 NA 17.0 NA 2 104 #> 105 6 120.0 0 NA 1.7 NA 1 105 #> 106 6 120.0 0 NA 18.0 NA 2 106 #> 107 6 144.0 0 NA 25.0 NA 2 107 #> 108 7 0.0 1 90.0 NA 1 NA 108 #> 109 7 3.0 0 NA 13.4 NA 1 109 #> 110 7 6.0 0 NA 12.4 NA 1 110 #> 111 7 9.0 0 NA 12.7 NA 1 111 #> 112 7 12.0 0 NA 8.8 NA 1 112 #> 113 7 24.0 0 NA 6.1 NA 1 113 #> 114 7 24.0 0 NA 36.0 NA 2 114 #> 115 7 36.0 0 NA 3.5 NA 1 115 #> 116 7 36.0 0 NA 33.0 NA 2 116 #> 117 7 48.0 0 NA 1.8 NA 1 117 #> 118 7 48.0 0 NA 28.0 NA 2 118 #> 119 7 72.0 0 NA 1.5 NA 1 119 #> 120 7 72.0 0 NA 52.0 NA 2 120 #> 121 7 96.0 0 NA 1.0 NA 1 121 #> 122 7 96.0 0 NA 80.0 NA 2 122 #> 123 7 120.0 0 NA 90.0 NA 2 123 #> 124 7 144.0 0 NA 100.0 NA 2 124 #> 125 8 0.0 1 135.0 NA 1 NA 125 #> 126 8 0.0 0 NA 88.0 NA 2 126 #> 127 8 2.0 0 NA 17.6 NA 1 127 #> 128 8 3.0 0 NA 17.3 NA 1 128 #> 129 8 6.0 0 NA 15.0 NA 1 129 #> 130 8 9.0 0 NA 15.0 NA 1 130 #> 131 8 12.0 0 NA 12.4 NA 1 131 #> 132 8 24.0 0 NA 7.9 NA 1 132 #> 133 8 24.0 0 NA 35.0 NA 2 133 #> 134 8 36.0 0 NA 7.9 NA 1 134 #> 135 8 36.0 0 NA 20.0 NA 2 135 #> 136 8 48.0 0 NA 5.1 NA 1 136 #> 137 8 48.0 0 NA 12.0 NA 2 137 #> 138 8 72.0 0 NA 3.6 NA 1 138 #> 139 8 72.0 0 NA 16.0 NA 2 139 #> 140 8 96.0 0 NA 2.4 NA 1 140 #> 141 8 96.0 0 NA 23.0 NA 2 141 #> 142 8 120.0 0 NA 2.0 NA 1 142 #> 143 8 120.0 0 NA 36.0 NA 2 143 #> 144 8 144.0 0 NA 48.0 NA 2 144 #> 145 9 0.0 1 75.0 NA 1 NA 145 #> 146 9 0.0 0 NA 92.0 NA 2 146 #> 147 9 0.5 0 NA 0.0 NA 1 147 #> 148 9 1.0 0 NA 1.0 NA 1 148 #> 149 9 2.0 0 NA 4.6 NA 1 149 #> 150 9 3.0 0 NA 12.7 NA 1 150 #> 151 9 3.0 0 NA 8.0 NA 1 151 #> 152 9 6.0 0 NA 12.7 NA 1 152 #> 153 9 6.0 0 NA 11.5 NA 1 153 #> 154 9 9.0 0 NA 12.9 NA 1 154 #> 155 9 9.0 0 NA 11.4 NA 1 155 #> 156 9 12.0 0 NA 11.4 NA 1 156 #> 157 9 12.0 0 NA 11.0 NA 1 157 #> 158 9 24.0 0 NA 9.1 NA 1 158 #> 159 9 24.0 0 NA 33.0 NA 2 159 #> 160 9 36.0 0 NA 8.2 NA 1 160 #> 161 9 36.0 0 NA 22.0 NA 2 161 #> 162 9 48.0 0 NA 5.9 NA 1 162 #> 163 9 48.0 0 NA 16.0 NA 2 163 #> 164 9 72.0 0 NA 3.6 NA 1 164 #> 165 9 72.0 0 NA 18.0 NA 2 165 #> 166 9 96.0 0 NA 1.7 NA 1 166 #> 167 9 96.0 0 NA 32.0 NA 2 167 #> 168 9 120.0 0 NA 1.1 NA 1 168 #> 169 9 120.0 0 NA 30.0 NA 2 169 #> 170 9 144.0 0 NA 45.0 NA 2 170 #> 171 10 0.0 1 105.0 NA 1 NA 171 #> 172 10 0.0 0 NA 90.0 NA 2 172 #> 173 10 24.0 0 NA 8.6 NA 1 173 #> 174 10 24.0 0 NA 39.0 NA 2 174 #> 175 10 36.0 0 NA 8.0 NA 1 175 #> 176 10 36.0 0 NA 22.0 NA 2 176 #> 177 10 48.0 0 NA 6.0 NA 1 177 #> 178 10 48.0 0 NA 17.0 NA 2 178 #> 179 10 72.0 0 NA 4.4 NA 1 179 #> 180 10 72.0 0 NA 17.0 NA 2 180 #> 181 10 96.0 0 NA 3.6 NA 1 181 #> 182 10 96.0 0 NA 22.0 NA 2 182 #> 183 10 120.0 0 NA 2.8 NA 1 183 #> 184 10 120.0 0 NA 25.0 NA 2 184 #> 185 10 144.0 0 NA 33.0 NA 2 185 #> 186 11 0.0 1 123.0 NA 1 NA 186 #> 187 11 0.0 0 NA 100.0 NA 2 187 #> 188 11 1.5 0 NA 11.4 NA 1 188 #> 189 11 3.0 0 NA 15.4 NA 1 189 #> 190 11 6.0 0 NA 17.5 NA 1 190 #> 191 11 12.0 0 NA 14.0 NA 1 191 #> 192 11 24.0 0 NA 9.0 NA 1 192 #> 193 11 24.0 0 NA 37.0 NA 2 193 #> 194 11 36.0 0 NA 8.9 NA 1 194 #> 195 11 36.0 0 NA 24.0 NA 2 195 #> 196 11 48.0 0 NA 6.6 NA 1 196 #> 197 11 48.0 0 NA 14.0 NA 2 197 #> 198 11 72.0 0 NA 4.2 NA 1 198 #> 199 11 72.0 0 NA 11.0 NA 2 199 #> 200 11 96.0 0 NA 3.6 NA 1 200 #> 201 11 96.0 0 NA 14.0 NA 2 201 #> 202 11 120.0 0 NA 2.6 NA 1 202 #> 203 11 120.0 0 NA 23.0 NA 2 203 #> 204 11 144.0 0 NA 33.0 NA 2 204 #> 205 12 0.0 1 113.0 NA 1 NA 205 #> 206 12 0.0 0 NA 85.0 NA 2 206 #> 207 12 1.5 0 NA 0.6 NA 1 207 #> 208 12 3.0 0 NA 2.8 NA 1 208 #> 209 12 6.0 0 NA 13.8 NA 1 209 #> 210 12 9.0 0 NA 15.0 NA 1 210 #> 211 12 24.0 0 NA 10.5 NA 1 211 #> 212 12 24.0 0 NA 25.0 NA 2 212 #> 213 12 36.0 0 NA 9.1 NA 1 213 #> 214 12 36.0 0 NA 15.0 NA 2 214 #> 215 12 48.0 0 NA 6.6 NA 1 215 #> 216 12 48.0 0 NA 11.0 NA 2 216 #> 217 12 72.0 0 NA 4.9 NA 1 217 #> 218 12 96.0 0 NA 2.4 NA 1 218 #> 219 12 120.0 0 NA 1.9 NA 1 219 #> 220 13 0.0 1 113.0 NA 1 NA 220 #> 221 13 0.0 0 NA 88.0 NA 2 221 #> 222 13 1.5 0 NA 3.6 NA 1 222 #> 223 13 3.0 0 NA 12.9 NA 1 223 #> 224 13 6.0 0 NA 12.9 NA 1 224 #> 225 13 9.0 0 NA 10.2 NA 1 225 #> 226 13 24.0 0 NA 6.4 NA 1 226 #> 227 13 24.0 0 NA 41.0 NA 2 227 #> 228 13 36.0 0 NA 6.9 NA 1 228 #> 229 13 36.0 0 NA 23.0 NA 2 229 #> 230 13 48.0 0 NA 4.5 NA 1 230 #> 231 13 48.0 0 NA 16.0 NA 2 231 #> 232 13 72.0 0 NA 3.2 NA 1 232 #> 233 13 72.0 0 NA 14.0 NA 2 233 #> 234 13 96.0 0 NA 2.4 NA 1 234 #> 235 13 96.0 0 NA 18.0 NA 2 235 #> 236 13 120.0 0 NA 1.3 NA 1 236 #> 237 13 120.0 0 NA 22.0 NA 2 237 #> 238 13 144.0 0 NA 35.0 NA 2 238 #> 239 14 0.0 1 75.0 NA 1 NA 239 #> 240 14 0.0 0 NA 85.0 NA 2 240 #> 241 14 0.5 0 NA 0.0 NA 1 241 #> 242 14 1.0 0 NA 2.7 NA 1 242 #> 243 14 2.0 0 NA 11.6 NA 1 243 #> 244 14 3.0 0 NA 11.6 NA 1 244 #> 245 14 6.0 0 NA 11.3 NA 1 245 #> 246 14 9.0 0 NA 9.7 NA 1 246 #> 247 14 24.0 0 NA 6.5 NA 1 247 #> 248 14 24.0 0 NA 32.0 NA 2 248 #> 249 14 36.0 0 NA 5.2 NA 1 249 #> 250 14 36.0 0 NA 22.0 NA 2 250 #> 251 14 48.0 0 NA 3.6 NA 1 251 #> 252 14 48.0 0 NA 21.0 NA 2 252 #> 253 14 72.0 0 NA 2.4 NA 1 253 #> 254 14 72.0 0 NA 28.0 NA 2 254 #> 255 14 96.0 0 NA 0.9 NA 1 255 #> 256 14 96.0 0 NA 38.0 NA 2 256 #> 257 14 120.0 0 NA 46.0 NA 2 257 #> 258 14 144.0 0 NA 65.0 NA 2 258 #> 259 15 0.0 1 85.0 NA 1 NA 259 #> 260 15 0.0 0 NA 100.0 NA 2 260 #> 261 15 1.0 0 NA 6.6 NA 1 261 #> 262 15 3.0 0 NA 11.9 NA 1 262 #> 263 15 6.0 0 NA 11.7 NA 1 263 #> 264 15 9.0 0 NA 12.2 NA 1 264 #> 265 15 24.0 0 NA 8.1 NA 1 265 #> 266 15 24.0 0 NA 43.0 NA 2 266 #> 267 15 36.0 0 NA 7.4 NA 1 267 #> 268 15 36.0 0 NA 26.0 NA 2 268 #> 269 15 48.0 0 NA 6.8 NA 1 269 #> 270 15 48.0 0 NA 15.0 NA 2 270 #> 271 15 72.0 0 NA 5.3 NA 1 271 #> 272 15 72.0 0 NA 13.0 NA 2 272 #> 273 15 96.0 0 NA 3.0 NA 1 273 #> 274 15 96.0 0 NA 21.0 NA 2 274 #> 275 15 120.0 0 NA 2.0 NA 1 275 #> 276 15 120.0 0 NA 28.0 NA 2 276 #> 277 15 144.0 0 NA 39.0 NA 2 277 #> 278 16 0.0 1 87.0 NA 1 NA 278 #> 279 16 0.0 0 NA 100.0 NA 2 279 #> 280 16 24.0 0 NA 10.4 NA 1 280 #> 281 16 24.0 0 NA 42.0 NA 2 281 #> 282 16 36.0 0 NA 8.9 NA 1 282 #> 283 16 36.0 0 NA 32.0 NA 2 283 #> 284 16 48.0 0 NA 7.0 NA 1 284 #> 285 16 48.0 0 NA 26.0 NA 2 285 #> 286 16 72.0 0 NA 4.4 NA 1 286 #> 287 16 72.0 0 NA 31.0 NA 2 287 #> 288 16 96.0 0 NA 3.2 NA 1 288 #> 289 16 96.0 0 NA 33.0 NA 2 289 #> 290 16 120.0 0 NA 2.4 NA 1 290 #> 291 16 120.0 0 NA 54.0 NA 2 291 #> 292 17 0.0 1 117.0 NA 1 NA 292 #> 293 17 0.0 0 NA 100.0 NA 2 293 #> 294 17 24.0 0 NA 7.6 NA 1 294 #> 295 17 24.0 0 NA 35.0 NA 2 295 #> 296 17 36.0 0 NA 6.4 NA 1 296 #> 297 17 36.0 0 NA 23.0 NA 2 297 #> 298 17 48.0 0 NA 6.0 NA 1 298 #> 299 17 48.0 0 NA 17.0 NA 2 299 #> 300 17 72.0 0 NA 4.0 NA 1 300 #> 301 17 72.0 0 NA 18.0 NA 2 301 #> 302 17 96.0 0 NA 3.1 NA 1 302 #> 303 17 96.0 0 NA 18.0 NA 2 303 #> 304 17 120.0 0 NA 2.0 NA 1 304 #> 305 17 120.0 0 NA 21.0 NA 2 305 #> 306 18 0.0 1 112.0 NA 1 NA 306 #> 307 18 0.0 0 NA 100.0 NA 2 307 #> 308 18 24.0 0 NA 7.6 NA 1 308 #> 309 18 24.0 0 NA 32.0 NA 2 309 #> 310 18 36.0 0 NA 6.6 NA 1 310 #> 311 18 36.0 0 NA 20.0 NA 2 311 #> 312 18 48.0 0 NA 5.4 NA 1 312 #> 313 18 48.0 0 NA 18.0 NA 2 313 #> 314 18 72.0 0 NA 3.4 NA 1 314 #> 315 18 72.0 0 NA 18.0 NA 2 315 #> 316 18 96.0 0 NA 1.2 NA 1 316 #> 317 18 96.0 0 NA 19.0 NA 2 317 #> 318 18 120.0 0 NA 0.9 NA 1 318 #> 319 18 120.0 0 NA 29.0 NA 2 319 #> 320 19 0.0 1 95.5 NA 1 NA 320 #> 321 19 0.0 0 NA 100.0 NA 2 321 #> 322 19 24.0 0 NA 6.6 NA 1 322 #> 323 19 24.0 0 NA 33.0 NA 2 323 #> 324 19 36.0 0 NA 5.3 NA 1 324 #> 325 19 36.0 0 NA 28.0 NA 2 325 #> 326 19 48.0 0 NA 3.6 NA 1 326 #> 327 19 48.0 0 NA 18.0 NA 2 327 #> 328 19 72.0 0 NA 2.7 NA 1 328 #> 329 19 72.0 0 NA 18.0 NA 2 329 #> 330 19 96.0 0 NA 1.4 NA 1 330 #> 331 19 96.0 0 NA 17.0 NA 2 331 #> 332 19 120.0 0 NA 1.1 NA 1 332 #> 333 19 120.0 0 NA 26.0 NA 2 333 #> 334 20 0.0 1 88.5 NA 1 NA 334 #> 335 20 0.0 0 NA 100.0 NA 2 335 #> 336 20 24.0 0 NA 9.6 NA 1 336 #> 337 20 24.0 0 NA 41.0 NA 2 337 #> 338 20 36.0 0 NA 8.0 NA 1 338 #> 339 20 36.0 0 NA 30.0 NA 2 339 #> 340 20 48.0 0 NA 6.6 NA 1 340 #> 341 20 48.0 0 NA 22.0 NA 2 341 #> 342 20 72.0 0 NA 5.6 NA 1 342 #> 343 20 72.0 0 NA 23.0 NA 2 343 #> 344 20 96.0 0 NA 3.5 NA 1 344 #> 345 20 96.0 0 NA 23.0 NA 2 345 #> 346 20 120.0 0 NA 2.3 NA 1 346 #> 347 20 120.0 0 NA 35.0 NA 2 347 #> 348 21 0.0 1 93.0 NA 1 NA 348 #> 349 21 0.0 0 NA 100.0 NA 2 349 #> 350 21 24.0 0 NA 7.3 NA 1 350 #> 351 21 24.0 0 NA 46.0 NA 2 351 #> 352 21 36.0 0 NA 6.1 NA 1 352 #> 353 21 36.0 0 NA 27.0 NA 2 353 #> 354 21 48.0 0 NA 4.3 NA 1 354 #> 355 21 48.0 0 NA 22.0 NA 2 355 #> 356 21 72.0 0 NA 3.2 NA 1 356 #> 357 21 72.0 0 NA 36.0 NA 2 357 #> 358 21 96.0 0 NA 2.3 NA 1 358 #> 359 21 96.0 0 NA 40.0 NA 2 359 #> 360 21 120.0 0 NA 1.9 NA 1 360 #> 361 21 120.0 0 NA 44.0 NA 2 361 #> 362 22 0.0 1 87.0 NA 1 NA 362 #> 363 22 0.0 0 NA 100.0 NA 2 363 #> 364 22 24.0 0 NA 8.9 NA 1 364 #> 365 22 24.0 0 NA 35.0 NA 2 365 #> 366 22 36.0 0 NA 8.4 NA 1 366 #> 367 22 36.0 0 NA 27.0 NA 2 367 #> 368 22 48.0 0 NA 8.0 NA 1 368 #> 369 22 48.0 0 NA 23.0 NA 2 369 #> 370 22 72.0 0 NA 4.4 NA 1 370 #> 371 22 72.0 0 NA 27.0 NA 2 371 #> 372 22 96.0 0 NA 3.2 NA 1 372 #> 373 22 96.0 0 NA 43.0 NA 2 373 #> 374 22 120.0 0 NA 1.7 NA 1 374 #> 375 22 120.0 0 NA 43.0 NA 2 375 #> 376 23 0.0 1 110.0 NA 1 NA 376 #> 377 23 0.0 0 NA 100.0 NA 2 377 #> 378 23 24.0 0 NA 9.8 NA 1 378 #> 379 23 24.0 0 NA 34.0 NA 2 379 #> 380 23 36.0 0 NA 8.4 NA 1 380 #> 381 23 36.0 0 NA 24.0 NA 2 381 #> 382 23 48.0 0 NA 6.6 NA 1 382 #> 383 23 48.0 0 NA 15.0 NA 2 383 #> 384 23 72.0 0 NA 4.8 NA 1 384 #> 385 23 72.0 0 NA 15.0 NA 2 385 #> 386 23 96.0 0 NA 3.2 NA 1 386 #> 387 23 96.0 0 NA 19.0 NA 2 387 #> 388 23 120.0 0 NA 2.4 NA 1 388 #> 389 23 120.0 0 NA 19.0 NA 2 389 #> 390 24 0.0 1 115.0 NA 1 NA 390 #> 391 24 0.0 0 NA 88.0 NA 2 391 #> 392 24 24.0 0 NA 8.2 NA 1 392 #> 393 24 24.0 0 NA 37.0 NA 2 393 #> 394 24 36.0 0 NA 7.5 NA 1 394 #> 395 24 36.0 0 NA 20.0 NA 2 395 #> 396 24 48.0 0 NA 6.8 NA 1 396 #> 397 24 48.0 0 NA 20.0 NA 2 397 #> 398 24 72.0 0 NA 5.5 NA 1 398 #> 399 24 72.0 0 NA 26.0 NA 2 399 #> 400 24 96.0 0 NA 4.5 NA 1 400 #> 401 24 96.0 0 NA 28.0 NA 2 401 #> 402 24 120.0 0 NA 3.7 NA 1 402 #> 403 24 120.0 0 NA 50.0 NA 2 403 #> 404 25 0.0 1 112.0 NA 1 NA 404 #> 405 25 0.0 0 NA 100.0 NA 2 405 #> 406 25 24.0 0 NA 11.0 NA 1 406 #> 407 25 24.0 0 NA 32.0 NA 2 407 #> 408 25 36.0 0 NA 10.0 NA 1 408 #> 409 25 36.0 0 NA 20.0 NA 2 409 #> 410 25 48.0 0 NA 8.2 NA 1 410 #> 411 25 48.0 0 NA 17.0 NA 2 411 #> 412 25 72.0 0 NA 6.0 NA 1 412 #> 413 25 72.0 0 NA 19.0 NA 2 413 #> 414 25 96.0 0 NA 3.7 NA 1 414 #> 415 25 96.0 0 NA 21.0 NA 2 415 #> 416 25 120.0 0 NA 2.6 NA 1 416 #> 417 25 120.0 0 NA 30.0 NA 2 417 #> 418 26 0.0 1 120.0 NA 1 NA 418 #> 419 26 0.0 0 NA 100.0 NA 2 419 #> 420 26 24.0 0 NA 10.0 NA 1 420 #> 421 26 24.0 0 NA 41.0 NA 2 421 #> 422 26 36.0 0 NA 9.0 NA 1 422 #> 423 26 36.0 0 NA 28.0 NA 2 423 #> 424 26 48.0 0 NA 7.3 NA 1 424 #> 425 26 48.0 0 NA 19.0 NA 2 425 #> 426 26 72.0 0 NA 5.2 NA 1 426 #> 427 26 72.0 0 NA 17.0 NA 2 427 #> 428 26 96.0 0 NA 3.7 NA 1 428 #> 429 26 96.0 0 NA 17.0 NA 2 429 #> 430 26 120.0 0 NA 2.7 NA 1 430 #> 431 26 120.0 0 NA 24.0 NA 2 431 #> 432 27 0.0 1 120.0 NA 1 NA 432 #> 433 27 0.0 0 NA 100.0 NA 2 433 #> 434 27 24.0 0 NA 11.8 NA 1 434 #> 435 27 24.0 0 NA 32.0 NA 2 435 #> 436 27 36.0 0 NA 9.2 NA 1 436 #> 437 27 36.0 0 NA 21.0 NA 2 437 #> 438 27 48.0 0 NA 7.7 NA 1 438 #> 439 27 48.0 0 NA 19.0 NA 2 439 #> 440 27 72.0 0 NA 4.9 NA 1 440 #> 441 27 72.0 0 NA 22.0 NA 2 441 #> 442 27 96.0 0 NA 3.4 NA 1 442 #> 443 27 96.0 0 NA 33.0 NA 2 443 #> 444 27 120.0 0 NA 2.7 NA 1 444 #> 445 27 120.0 0 NA 46.0 NA 2 445 #> 446 28 0.0 1 120.0 NA 1 NA 446 #> 447 28 0.0 0 NA 100.0 NA 2 447 #> 448 28 24.0 0 NA 10.1 NA 1 448 #> 449 28 24.0 0 NA 39.0 NA 2 449 #> 450 28 36.0 0 NA 8.0 NA 1 450 #> 451 28 36.0 0 NA 25.0 NA 2 451 #> 452 28 48.0 0 NA 6.0 NA 1 452 #> 453 28 48.0 0 NA 16.0 NA 2 453 #> 454 28 72.0 0 NA 4.9 NA 1 454 #> 455 28 72.0 0 NA 14.0 NA 2 455 #> 456 28 96.0 0 NA 3.4 NA 1 456 #> 457 28 96.0 0 NA 15.0 NA 2 457 #> 458 28 120.0 0 NA 2.0 NA 1 458 #> 459 28 120.0 0 NA 20.0 NA 2 459 #> 460 29 0.0 1 153.0 NA 1 NA 460 #> 461 29 0.0 0 NA 86.0 NA 2 461 #> 462 29 24.0 0 NA 8.3 NA 1 462 #> 463 29 24.0 0 NA 35.0 NA 2 463 #> 464 29 36.0 0 NA 7.0 NA 1 464 #> 465 29 36.0 0 NA 21.0 NA 2 465 #> 466 29 48.0 0 NA 5.6 NA 1 466 #> 467 29 48.0 0 NA 18.0 NA 2 467 #> 468 29 72.0 0 NA 4.1 NA 1 468 #> 469 29 72.0 0 NA 20.0 NA 2 469 #> 470 29 96.0 0 NA 3.1 NA 1 470 #> 471 29 96.0 0 NA 29.0 NA 2 471 #> 472 29 120.0 0 NA 2.2 NA 1 472 #> 473 29 120.0 0 NA 41.0 NA 2 473 #> 474 30 0.0 1 105.0 NA 1 NA 474 #> 475 30 0.0 0 NA 100.0 NA 2 475 #> 476 30 24.0 0 NA 9.9 NA 1 476 #> 477 30 24.0 0 NA 45.0 NA 2 477 #> 478 30 36.0 0 NA 7.5 NA 1 478 #> 479 30 36.0 0 NA 24.0 NA 2 479 #> 480 30 48.0 0 NA 6.5 NA 1 480 #> 481 30 48.0 0 NA 23.0 NA 2 481 #> 482 30 72.0 0 NA 4.1 NA 1 482 #> 483 30 72.0 0 NA 26.0 NA 2 483 #> 484 30 96.0 0 NA 2.9 NA 1 484 #> 485 30 96.0 0 NA 28.0 NA 2 485 #> 486 30 120.0 0 NA 2.3 NA 1 486 #> 487 30 120.0 0 NA 39.0 NA 2 487 #> 488 31 0.0 1 125.0 NA 1 NA 488 #> 489 31 0.0 0 NA 100.0 NA 2 489 #> 490 31 24.0 0 NA 9.5 NA 1 490 #> 491 31 24.0 0 NA 45.0 NA 2 491 #> 492 31 36.0 0 NA 7.8 NA 1 492 #> 493 31 36.0 0 NA 30.0 NA 2 493 #> 494 31 48.0 0 NA 6.4 NA 1 494 #> 495 31 48.0 0 NA 24.0 NA 2 495 #> 496 31 72.0 0 NA 4.5 NA 1 496 #> 497 31 72.0 0 NA 22.0 NA 2 497 #> 498 31 96.0 0 NA 3.4 NA 1 498 #> 499 31 96.0 0 NA 28.0 NA 2 499 #> 500 31 120.0 0 NA 2.5 NA 1 500 #> 501 31 120.0 0 NA 42.0 NA 2 501 #> 502 32 0.0 1 93.0 NA 1 NA 502 #> 503 32 0.0 0 NA 100.0 NA 2 503 #> 504 32 24.0 0 NA 8.9 NA 1 504 #> 505 32 24.0 0 NA 36.0 NA 2 505 #> 506 32 36.0 0 NA 7.7 NA 1 506 #> 507 32 36.0 0 NA 27.0 NA 2 507 #> 508 32 48.0 0 NA 6.9 NA 1 508 #> 509 32 48.0 0 NA 24.0 NA 2 509 #> 510 32 72.0 0 NA 4.4 NA 1 510 #> 511 32 72.0 0 NA 23.0 NA 2 511 #> 512 32 96.0 0 NA 3.5 NA 1 512 #> 513 32 96.0 0 NA 20.0 NA 2 513 #> 514 32 120.0 0 NA 2.5 NA 1 514 #> 515 32 120.0 0 NA 22.0 NA 2 515 bblDatToMrgsolve(pk.turnover.emax3, nlmixr2data::warfarin) #> #> #> ID TIME EVID AMT II DV CMT DVID SS nlmixrRowNums #> 1 1 0.0 1 100.0 0 NA 1 0 0 1 #> 2 1 0.5 0 NA 0 0.0 5 1 0 2 #> 3 1 1.0 0 NA 0 1.9 5 1 0 3 #> 4 1 2.0 0 NA 0 3.3 5 1 0 4 #> 5 1 3.0 0 NA 0 6.6 5 1 0 5 #> 6 1 6.0 0 NA 0 9.1 5 1 0 6 #> 7 1 9.0 0 NA 0 10.8 5 1 0 7 #> 8 1 12.0 0 NA 0 8.6 5 1 0 8 #> 9 1 24.0 0 NA 0 5.6 5 1 0 9 #> 10 1 24.0 0 NA 0 44.0 6 2 0 10 #> 11 1 36.0 0 NA 0 4.0 5 1 0 11 #> 12 1 36.0 0 NA 0 27.0 6 2 0 12 #> 13 1 48.0 0 NA 0 2.7 5 1 0 13 #> 14 1 48.0 0 NA 0 28.0 6 2 0 14 #> 15 1 72.0 0 NA 0 0.8 5 1 0 15 #> 16 1 72.0 0 NA 0 31.0 6 2 0 16 #> 17 1 96.0 0 NA 0 60.0 6 2 0 17 #> 18 1 120.0 0 NA 0 65.0 6 2 0 18 #> 19 1 144.0 0 NA 0 71.0 6 2 0 19 #> 20 2 0.0 1 100.0 0 NA 1 0 0 20 #> 21 2 0.0 0 NA 0 100.0 6 2 0 21 #> 22 2 24.0 0 NA 0 9.2 5 1 0 22 #> 23 2 24.0 0 NA 0 49.0 6 2 0 23 #> 24 2 36.0 0 NA 0 8.5 5 1 0 24 #> 25 2 36.0 0 NA 0 32.0 6 2 0 25 #> 26 2 48.0 0 NA 0 6.4 5 1 0 26 #> 27 2 48.0 0 NA 0 26.0 6 2 0 27 #> 28 2 72.0 0 NA 0 4.8 5 1 0 28 #> 29 2 72.0 0 NA 0 22.0 6 2 0 29 #> 30 2 96.0 0 NA 0 3.1 5 1 0 30 #> 31 2 96.0 0 NA 0 28.0 6 2 0 31 #> 32 2 120.0 0 NA 0 2.5 5 1 0 32 #> 33 2 120.0 0 NA 0 33.0 6 2 0 33 #> 34 3 0.0 1 100.0 0 NA 1 0 0 34 #> 35 3 0.0 0 NA 0 100.0 6 2 0 35 #> 36 3 0.5 0 NA 0 0.0 5 1 0 36 #> 37 3 2.0 0 NA 0 8.4 5 1 0 37 #> 38 3 3.0 0 NA 0 9.7 5 1 0 38 #> 39 3 6.0 0 NA 0 9.8 5 1 0 39 #> 40 3 12.0 0 NA 0 11.0 5 1 0 40 #> 41 3 24.0 0 NA 0 8.3 5 1 0 41 #> 42 3 24.0 0 NA 0 46.0 6 2 0 42 #> 43 3 36.0 0 NA 0 7.7 5 1 0 43 #> 44 3 36.0 0 NA 0 22.0 6 2 0 44 #> 45 3 48.0 0 NA 0 6.3 5 1 0 45 #> 46 3 48.0 0 NA 0 19.0 6 2 0 46 #> 47 3 72.0 0 NA 0 4.1 5 1 0 47 #> 48 3 72.0 0 NA 0 20.0 6 2 0 48 #> 49 3 96.0 0 NA 0 3.0 5 1 0 49 #> 50 3 96.0 0 NA 0 42.0 6 2 0 50 #> 51 3 120.0 0 NA 0 1.4 5 1 0 51 #> 52 3 120.0 0 NA 0 49.0 6 2 0 52 #> 53 3 144.0 0 NA 0 54.0 6 2 0 53 #> 54 4 0.0 1 120.0 0 NA 1 0 0 54 #> 55 4 0.0 0 NA 0 100.0 6 2 0 55 #> 56 4 3.0 0 NA 0 12.0 5 1 0 56 #> 57 4 6.0 0 NA 0 13.2 5 1 0 57 #> 58 4 9.0 0 NA 0 14.4 5 1 0 58 #> 59 4 24.0 0 NA 0 9.6 5 1 0 59 #> 60 4 24.0 0 NA 0 30.0 6 2 0 60 #> 61 4 36.0 0 NA 0 8.2 5 1 0 61 #> 62 4 36.0 0 NA 0 24.0 6 2 0 62 #> 63 4 48.0 0 NA 0 7.8 5 1 0 63 #> 64 4 48.0 0 NA 0 13.0 6 2 0 64 #> 65 4 72.0 0 NA 0 5.8 5 1 0 65 #> 66 4 72.0 0 NA 0 9.0 6 2 0 66 #> 67 4 96.0 0 NA 0 4.3 5 1 0 67 #> 68 4 96.0 0 NA 0 9.0 6 2 0 68 #> 69 4 120.0 0 NA 0 3.0 5 1 0 69 #> 70 4 120.0 0 NA 0 11.0 6 2 0 70 #> 71 4 144.0 0 NA 0 12.0 6 2 0 71 #> 72 5 0.0 1 60.0 0 NA 1 0 0 72 #> 73 5 0.0 0 NA 0 82.0 6 2 0 73 #> 74 5 3.0 0 NA 0 11.1 5 1 0 74 #> 75 5 6.0 0 NA 0 11.9 5 1 0 75 #> 76 5 9.0 0 NA 0 9.8 5 1 0 76 #> 77 5 12.0 0 NA 0 11.0 5 1 0 77 #> 78 5 24.0 0 NA 0 8.5 5 1 0 78 #> 79 5 24.0 0 NA 0 43.0 6 2 0 79 #> 80 5 36.0 0 NA 0 7.6 5 1 0 80 #> 81 5 36.0 0 NA 0 25.0 6 2 0 81 #> 82 5 48.0 0 NA 0 5.4 5 1 0 82 #> 83 5 48.0 0 NA 0 18.0 6 2 0 83 #> 84 5 72.0 0 NA 0 4.5 5 1 0 84 #> 85 5 72.0 0 NA 0 17.0 6 2 0 85 #> 86 5 96.0 0 NA 0 3.3 5 1 0 86 #> 87 5 96.0 0 NA 0 23.0 6 2 0 87 #> 88 5 120.0 0 NA 0 2.3 5 1 0 88 #> 89 5 120.0 0 NA 0 29.0 6 2 0 89 #> 90 5 144.0 0 NA 0 41.0 6 2 0 90 #> 91 6 0.0 1 113.0 0 NA 1 0 0 91 #> 92 6 0.0 0 NA 0 100.0 6 2 0 92 #> 93 6 6.0 0 NA 0 8.6 5 1 0 93 #> 94 6 12.0 0 NA 0 8.6 5 1 0 94 #> 95 6 24.0 0 NA 0 7.0 5 1 0 95 #> 96 6 24.0 0 NA 0 34.0 6 2 0 96 #> 97 6 36.0 0 NA 0 5.7 5 1 0 97 #> 98 6 36.0 0 NA 0 23.0 6 2 0 98 #> 99 6 48.0 0 NA 0 4.7 5 1 0 99 #> 100 6 48.0 0 NA 0 20.0 6 2 0 100 #> 101 6 72.0 0 NA 0 3.3 5 1 0 101 #> 102 6 72.0 0 NA 0 16.0 6 2 0 102 #> 103 6 96.0 0 NA 0 2.3 5 1 0 103 #> 104 6 96.0 0 NA 0 17.0 6 2 0 104 #> 105 6 120.0 0 NA 0 1.7 5 1 0 105 #> 106 6 120.0 0 NA 0 18.0 6 2 0 106 #> 107 6 144.0 0 NA 0 25.0 6 2 0 107 #> 108 7 0.0 1 90.0 0 NA 1 0 0 108 #> 109 7 3.0 0 NA 0 13.4 5 1 0 109 #> 110 7 6.0 0 NA 0 12.4 5 1 0 110 #> 111 7 9.0 0 NA 0 12.7 5 1 0 111 #> 112 7 12.0 0 NA 0 8.8 5 1 0 112 #> 113 7 24.0 0 NA 0 6.1 5 1 0 113 #> 114 7 24.0 0 NA 0 36.0 6 2 0 114 #> 115 7 36.0 0 NA 0 3.5 5 1 0 115 #> 116 7 36.0 0 NA 0 33.0 6 2 0 116 #> 117 7 48.0 0 NA 0 1.8 5 1 0 117 #> 118 7 48.0 0 NA 0 28.0 6 2 0 118 #> 119 7 72.0 0 NA 0 1.5 5 1 0 119 #> 120 7 72.0 0 NA 0 52.0 6 2 0 120 #> 121 7 96.0 0 NA 0 1.0 5 1 0 121 #> 122 7 96.0 0 NA 0 80.0 6 2 0 122 #> 123 7 120.0 0 NA 0 90.0 6 2 0 123 #> 124 7 144.0 0 NA 0 100.0 6 2 0 124 #> 125 8 0.0 1 135.0 0 NA 1 0 0 125 #> 126 8 0.0 0 NA 0 88.0 6 2 0 126 #> 127 8 2.0 0 NA 0 17.6 5 1 0 127 #> 128 8 3.0 0 NA 0 17.3 5 1 0 128 #> 129 8 6.0 0 NA 0 15.0 5 1 0 129 #> 130 8 9.0 0 NA 0 15.0 5 1 0 130 #> 131 8 12.0 0 NA 0 12.4 5 1 0 131 #> 132 8 24.0 0 NA 0 7.9 5 1 0 132 #> 133 8 24.0 0 NA 0 35.0 6 2 0 133 #> 134 8 36.0 0 NA 0 7.9 5 1 0 134 #> 135 8 36.0 0 NA 0 20.0 6 2 0 135 #> 136 8 48.0 0 NA 0 5.1 5 1 0 136 #> 137 8 48.0 0 NA 0 12.0 6 2 0 137 #> 138 8 72.0 0 NA 0 3.6 5 1 0 138 #> 139 8 72.0 0 NA 0 16.0 6 2 0 139 #> 140 8 96.0 0 NA 0 2.4 5 1 0 140 #> 141 8 96.0 0 NA 0 23.0 6 2 0 141 #> 142 8 120.0 0 NA 0 2.0 5 1 0 142 #> 143 8 120.0 0 NA 0 36.0 6 2 0 143 #> 144 8 144.0 0 NA 0 48.0 6 2 0 144 #> 145 9 0.0 1 75.0 0 NA 1 0 0 145 #> 146 9 0.0 0 NA 0 92.0 6 2 0 146 #> 147 9 0.5 0 NA 0 0.0 5 1 0 147 #> 148 9 1.0 0 NA 0 1.0 5 1 0 148 #> 149 9 2.0 0 NA 0 4.6 5 1 0 149 #> 150 9 3.0 0 NA 0 12.7 5 1 0 150 #> 151 9 3.0 0 NA 0 8.0 5 1 0 151 #> 152 9 6.0 0 NA 0 12.7 5 1 0 152 #> 153 9 6.0 0 NA 0 11.5 5 1 0 153 #> 154 9 9.0 0 NA 0 12.9 5 1 0 154 #> 155 9 9.0 0 NA 0 11.4 5 1 0 155 #> 156 9 12.0 0 NA 0 11.4 5 1 0 156 #> 157 9 12.0 0 NA 0 11.0 5 1 0 157 #> 158 9 24.0 0 NA 0 9.1 5 1 0 158 #> 159 9 24.0 0 NA 0 33.0 6 2 0 159 #> 160 9 36.0 0 NA 0 8.2 5 1 0 160 #> 161 9 36.0 0 NA 0 22.0 6 2 0 161 #> 162 9 48.0 0 NA 0 5.9 5 1 0 162 #> 163 9 48.0 0 NA 0 16.0 6 2 0 163 #> 164 9 72.0 0 NA 0 3.6 5 1 0 164 #> 165 9 72.0 0 NA 0 18.0 6 2 0 165 #> 166 9 96.0 0 NA 0 1.7 5 1 0 166 #> 167 9 96.0 0 NA 0 32.0 6 2 0 167 #> 168 9 120.0 0 NA 0 1.1 5 1 0 168 #> 169 9 120.0 0 NA 0 30.0 6 2 0 169 #> 170 9 144.0 0 NA 0 45.0 6 2 0 170 #> 171 10 0.0 1 105.0 0 NA 1 0 0 171 #> 172 10 0.0 0 NA 0 90.0 6 2 0 172 #> 173 10 24.0 0 NA 0 8.6 5 1 0 173 #> 174 10 24.0 0 NA 0 39.0 6 2 0 174 #> 175 10 36.0 0 NA 0 8.0 5 1 0 175 #> 176 10 36.0 0 NA 0 22.0 6 2 0 176 #> 177 10 48.0 0 NA 0 6.0 5 1 0 177 #> 178 10 48.0 0 NA 0 17.0 6 2 0 178 #> 179 10 72.0 0 NA 0 4.4 5 1 0 179 #> 180 10 72.0 0 NA 0 17.0 6 2 0 180 #> 181 10 96.0 0 NA 0 3.6 5 1 0 181 #> 182 10 96.0 0 NA 0 22.0 6 2 0 182 #> 183 10 120.0 0 NA 0 2.8 5 1 0 183 #> 184 10 120.0 0 NA 0 25.0 6 2 0 184 #> 185 10 144.0 0 NA 0 33.0 6 2 0 185 #> 186 11 0.0 1 123.0 0 NA 1 0 0 186 #> 187 11 0.0 0 NA 0 100.0 6 2 0 187 #> 188 11 1.5 0 NA 0 11.4 5 1 0 188 #> 189 11 3.0 0 NA 0 15.4 5 1 0 189 #> 190 11 6.0 0 NA 0 17.5 5 1 0 190 #> 191 11 12.0 0 NA 0 14.0 5 1 0 191 #> 192 11 24.0 0 NA 0 9.0 5 1 0 192 #> 193 11 24.0 0 NA 0 37.0 6 2 0 193 #> 194 11 36.0 0 NA 0 8.9 5 1 0 194 #> 195 11 36.0 0 NA 0 24.0 6 2 0 195 #> 196 11 48.0 0 NA 0 6.6 5 1 0 196 #> 197 11 48.0 0 NA 0 14.0 6 2 0 197 #> 198 11 72.0 0 NA 0 4.2 5 1 0 198 #> 199 11 72.0 0 NA 0 11.0 6 2 0 199 #> 200 11 96.0 0 NA 0 3.6 5 1 0 200 #> 201 11 96.0 0 NA 0 14.0 6 2 0 201 #> 202 11 120.0 0 NA 0 2.6 5 1 0 202 #> 203 11 120.0 0 NA 0 23.0 6 2 0 203 #> 204 11 144.0 0 NA 0 33.0 6 2 0 204 #> 205 12 0.0 1 113.0 0 NA 1 0 0 205 #> 206 12 0.0 0 NA 0 85.0 6 2 0 206 #> 207 12 1.5 0 NA 0 0.6 5 1 0 207 #> 208 12 3.0 0 NA 0 2.8 5 1 0 208 #> 209 12 6.0 0 NA 0 13.8 5 1 0 209 #> 210 12 9.0 0 NA 0 15.0 5 1 0 210 #> 211 12 24.0 0 NA 0 10.5 5 1 0 211 #> 212 12 24.0 0 NA 0 25.0 6 2 0 212 #> 213 12 36.0 0 NA 0 9.1 5 1 0 213 #> 214 12 36.0 0 NA 0 15.0 6 2 0 214 #> 215 12 48.0 0 NA 0 6.6 5 1 0 215 #> 216 12 48.0 0 NA 0 11.0 6 2 0 216 #> 217 12 72.0 0 NA 0 4.9 5 1 0 217 #> 218 12 96.0 0 NA 0 2.4 5 1 0 218 #> 219 12 120.0 0 NA 0 1.9 5 1 0 219 #> 220 13 0.0 1 113.0 0 NA 1 0 0 220 #> 221 13 0.0 0 NA 0 88.0 6 2 0 221 #> 222 13 1.5 0 NA 0 3.6 5 1 0 222 #> 223 13 3.0 0 NA 0 12.9 5 1 0 223 #> 224 13 6.0 0 NA 0 12.9 5 1 0 224 #> 225 13 9.0 0 NA 0 10.2 5 1 0 225 #> 226 13 24.0 0 NA 0 6.4 5 1 0 226 #> 227 13 24.0 0 NA 0 41.0 6 2 0 227 #> 228 13 36.0 0 NA 0 6.9 5 1 0 228 #> 229 13 36.0 0 NA 0 23.0 6 2 0 229 #> 230 13 48.0 0 NA 0 4.5 5 1 0 230 #> 231 13 48.0 0 NA 0 16.0 6 2 0 231 #> 232 13 72.0 0 NA 0 3.2 5 1 0 232 #> 233 13 72.0 0 NA 0 14.0 6 2 0 233 #> 234 13 96.0 0 NA 0 2.4 5 1 0 234 #> 235 13 96.0 0 NA 0 18.0 6 2 0 235 #> 236 13 120.0 0 NA 0 1.3 5 1 0 236 #> 237 13 120.0 0 NA 0 22.0 6 2 0 237 #> 238 13 144.0 0 NA 0 35.0 6 2 0 238 #> 239 14 0.0 1 75.0 0 NA 1 0 0 239 #> 240 14 0.0 0 NA 0 85.0 6 2 0 240 #> 241 14 0.5 0 NA 0 0.0 5 1 0 241 #> 242 14 1.0 0 NA 0 2.7 5 1 0 242 #> 243 14 2.0 0 NA 0 11.6 5 1 0 243 #> 244 14 3.0 0 NA 0 11.6 5 1 0 244 #> 245 14 6.0 0 NA 0 11.3 5 1 0 245 #> 246 14 9.0 0 NA 0 9.7 5 1 0 246 #> 247 14 24.0 0 NA 0 6.5 5 1 0 247 #> 248 14 24.0 0 NA 0 32.0 6 2 0 248 #> 249 14 36.0 0 NA 0 5.2 5 1 0 249 #> 250 14 36.0 0 NA 0 22.0 6 2 0 250 #> 251 14 48.0 0 NA 0 3.6 5 1 0 251 #> 252 14 48.0 0 NA 0 21.0 6 2 0 252 #> 253 14 72.0 0 NA 0 2.4 5 1 0 253 #> 254 14 72.0 0 NA 0 28.0 6 2 0 254 #> 255 14 96.0 0 NA 0 0.9 5 1 0 255 #> 256 14 96.0 0 NA 0 38.0 6 2 0 256 #> 257 14 120.0 0 NA 0 46.0 6 2 0 257 #> 258 14 144.0 0 NA 0 65.0 6 2 0 258 #> 259 15 0.0 1 85.0 0 NA 1 0 0 259 #> 260 15 0.0 0 NA 0 100.0 6 2 0 260 #> 261 15 1.0 0 NA 0 6.6 5 1 0 261 #> 262 15 3.0 0 NA 0 11.9 5 1 0 262 #> 263 15 6.0 0 NA 0 11.7 5 1 0 263 #> 264 15 9.0 0 NA 0 12.2 5 1 0 264 #> 265 15 24.0 0 NA 0 8.1 5 1 0 265 #> 266 15 24.0 0 NA 0 43.0 6 2 0 266 #> 267 15 36.0 0 NA 0 7.4 5 1 0 267 #> 268 15 36.0 0 NA 0 26.0 6 2 0 268 #> 269 15 48.0 0 NA 0 6.8 5 1 0 269 #> 270 15 48.0 0 NA 0 15.0 6 2 0 270 #> 271 15 72.0 0 NA 0 5.3 5 1 0 271 #> 272 15 72.0 0 NA 0 13.0 6 2 0 272 #> 273 15 96.0 0 NA 0 3.0 5 1 0 273 #> 274 15 96.0 0 NA 0 21.0 6 2 0 274 #> 275 15 120.0 0 NA 0 2.0 5 1 0 275 #> 276 15 120.0 0 NA 0 28.0 6 2 0 276 #> 277 15 144.0 0 NA 0 39.0 6 2 0 277 #> 278 16 0.0 1 87.0 0 NA 1 0 0 278 #> 279 16 0.0 0 NA 0 100.0 6 2 0 279 #> 280 16 24.0 0 NA 0 10.4 5 1 0 280 #> 281 16 24.0 0 NA 0 42.0 6 2 0 281 #> 282 16 36.0 0 NA 0 8.9 5 1 0 282 #> 283 16 36.0 0 NA 0 32.0 6 2 0 283 #> 284 16 48.0 0 NA 0 7.0 5 1 0 284 #> 285 16 48.0 0 NA 0 26.0 6 2 0 285 #> 286 16 72.0 0 NA 0 4.4 5 1 0 286 #> 287 16 72.0 0 NA 0 31.0 6 2 0 287 #> 288 16 96.0 0 NA 0 3.2 5 1 0 288 #> 289 16 96.0 0 NA 0 33.0 6 2 0 289 #> 290 16 120.0 0 NA 0 2.4 5 1 0 290 #> 291 16 120.0 0 NA 0 54.0 6 2 0 291 #> 292 17 0.0 1 117.0 0 NA 1 0 0 292 #> 293 17 0.0 0 NA 0 100.0 6 2 0 293 #> 294 17 24.0 0 NA 0 7.6 5 1 0 294 #> 295 17 24.0 0 NA 0 35.0 6 2 0 295 #> 296 17 36.0 0 NA 0 6.4 5 1 0 296 #> 297 17 36.0 0 NA 0 23.0 6 2 0 297 #> 298 17 48.0 0 NA 0 6.0 5 1 0 298 #> 299 17 48.0 0 NA 0 17.0 6 2 0 299 #> 300 17 72.0 0 NA 0 4.0 5 1 0 300 #> 301 17 72.0 0 NA 0 18.0 6 2 0 301 #> 302 17 96.0 0 NA 0 3.1 5 1 0 302 #> 303 17 96.0 0 NA 0 18.0 6 2 0 303 #> 304 17 120.0 0 NA 0 2.0 5 1 0 304 #> 305 17 120.0 0 NA 0 21.0 6 2 0 305 #> 306 18 0.0 1 112.0 0 NA 1 0 0 306 #> 307 18 0.0 0 NA 0 100.0 6 2 0 307 #> 308 18 24.0 0 NA 0 7.6 5 1 0 308 #> 309 18 24.0 0 NA 0 32.0 6 2 0 309 #> 310 18 36.0 0 NA 0 6.6 5 1 0 310 #> 311 18 36.0 0 NA 0 20.0 6 2 0 311 #> 312 18 48.0 0 NA 0 5.4 5 1 0 312 #> 313 18 48.0 0 NA 0 18.0 6 2 0 313 #> 314 18 72.0 0 NA 0 3.4 5 1 0 314 #> 315 18 72.0 0 NA 0 18.0 6 2 0 315 #> 316 18 96.0 0 NA 0 1.2 5 1 0 316 #> 317 18 96.0 0 NA 0 19.0 6 2 0 317 #> 318 18 120.0 0 NA 0 0.9 5 1 0 318 #> 319 18 120.0 0 NA 0 29.0 6 2 0 319 #> 320 19 0.0 1 95.5 0 NA 1 0 0 320 #> 321 19 0.0 0 NA 0 100.0 6 2 0 321 #> 322 19 24.0 0 NA 0 6.6 5 1 0 322 #> 323 19 24.0 0 NA 0 33.0 6 2 0 323 #> 324 19 36.0 0 NA 0 5.3 5 1 0 324 #> 325 19 36.0 0 NA 0 28.0 6 2 0 325 #> 326 19 48.0 0 NA 0 3.6 5 1 0 326 #> 327 19 48.0 0 NA 0 18.0 6 2 0 327 #> 328 19 72.0 0 NA 0 2.7 5 1 0 328 #> 329 19 72.0 0 NA 0 18.0 6 2 0 329 #> 330 19 96.0 0 NA 0 1.4 5 1 0 330 #> 331 19 96.0 0 NA 0 17.0 6 2 0 331 #> 332 19 120.0 0 NA 0 1.1 5 1 0 332 #> 333 19 120.0 0 NA 0 26.0 6 2 0 333 #> 334 20 0.0 1 88.5 0 NA 1 0 0 334 #> 335 20 0.0 0 NA 0 100.0 6 2 0 335 #> 336 20 24.0 0 NA 0 9.6 5 1 0 336 #> 337 20 24.0 0 NA 0 41.0 6 2 0 337 #> 338 20 36.0 0 NA 0 8.0 5 1 0 338 #> 339 20 36.0 0 NA 0 30.0 6 2 0 339 #> 340 20 48.0 0 NA 0 6.6 5 1 0 340 #> 341 20 48.0 0 NA 0 22.0 6 2 0 341 #> 342 20 72.0 0 NA 0 5.6 5 1 0 342 #> 343 20 72.0 0 NA 0 23.0 6 2 0 343 #> 344 20 96.0 0 NA 0 3.5 5 1 0 344 #> 345 20 96.0 0 NA 0 23.0 6 2 0 345 #> 346 20 120.0 0 NA 0 2.3 5 1 0 346 #> 347 20 120.0 0 NA 0 35.0 6 2 0 347 #> 348 21 0.0 1 93.0 0 NA 1 0 0 348 #> 349 21 0.0 0 NA 0 100.0 6 2 0 349 #> 350 21 24.0 0 NA 0 7.3 5 1 0 350 #> 351 21 24.0 0 NA 0 46.0 6 2 0 351 #> 352 21 36.0 0 NA 0 6.1 5 1 0 352 #> 353 21 36.0 0 NA 0 27.0 6 2 0 353 #> 354 21 48.0 0 NA 0 4.3 5 1 0 354 #> 355 21 48.0 0 NA 0 22.0 6 2 0 355 #> 356 21 72.0 0 NA 0 3.2 5 1 0 356 #> 357 21 72.0 0 NA 0 36.0 6 2 0 357 #> 358 21 96.0 0 NA 0 2.3 5 1 0 358 #> 359 21 96.0 0 NA 0 40.0 6 2 0 359 #> 360 21 120.0 0 NA 0 1.9 5 1 0 360 #> 361 21 120.0 0 NA 0 44.0 6 2 0 361 #> 362 22 0.0 1 87.0 0 NA 1 0 0 362 #> 363 22 0.0 0 NA 0 100.0 6 2 0 363 #> 364 22 24.0 0 NA 0 8.9 5 1 0 364 #> 365 22 24.0 0 NA 0 35.0 6 2 0 365 #> 366 22 36.0 0 NA 0 8.4 5 1 0 366 #> 367 22 36.0 0 NA 0 27.0 6 2 0 367 #> 368 22 48.0 0 NA 0 8.0 5 1 0 368 #> 369 22 48.0 0 NA 0 23.0 6 2 0 369 #> 370 22 72.0 0 NA 0 4.4 5 1 0 370 #> 371 22 72.0 0 NA 0 27.0 6 2 0 371 #> 372 22 96.0 0 NA 0 3.2 5 1 0 372 #> 373 22 96.0 0 NA 0 43.0 6 2 0 373 #> 374 22 120.0 0 NA 0 1.7 5 1 0 374 #> 375 22 120.0 0 NA 0 43.0 6 2 0 375 #> 376 23 0.0 1 110.0 0 NA 1 0 0 376 #> 377 23 0.0 0 NA 0 100.0 6 2 0 377 #> 378 23 24.0 0 NA 0 9.8 5 1 0 378 #> 379 23 24.0 0 NA 0 34.0 6 2 0 379 #> 380 23 36.0 0 NA 0 8.4 5 1 0 380 #> 381 23 36.0 0 NA 0 24.0 6 2 0 381 #> 382 23 48.0 0 NA 0 6.6 5 1 0 382 #> 383 23 48.0 0 NA 0 15.0 6 2 0 383 #> 384 23 72.0 0 NA 0 4.8 5 1 0 384 #> 385 23 72.0 0 NA 0 15.0 6 2 0 385 #> 386 23 96.0 0 NA 0 3.2 5 1 0 386 #> 387 23 96.0 0 NA 0 19.0 6 2 0 387 #> 388 23 120.0 0 NA 0 2.4 5 1 0 388 #> 389 23 120.0 0 NA 0 19.0 6 2 0 389 #> 390 24 0.0 1 115.0 0 NA 1 0 0 390 #> 391 24 0.0 0 NA 0 88.0 6 2 0 391 #> 392 24 24.0 0 NA 0 8.2 5 1 0 392 #> 393 24 24.0 0 NA 0 37.0 6 2 0 393 #> 394 24 36.0 0 NA 0 7.5 5 1 0 394 #> 395 24 36.0 0 NA 0 20.0 6 2 0 395 #> 396 24 48.0 0 NA 0 6.8 5 1 0 396 #> 397 24 48.0 0 NA 0 20.0 6 2 0 397 #> 398 24 72.0 0 NA 0 5.5 5 1 0 398 #> 399 24 72.0 0 NA 0 26.0 6 2 0 399 #> 400 24 96.0 0 NA 0 4.5 5 1 0 400 #> 401 24 96.0 0 NA 0 28.0 6 2 0 401 #> 402 24 120.0 0 NA 0 3.7 5 1 0 402 #> 403 24 120.0 0 NA 0 50.0 6 2 0 403 #> 404 25 0.0 1 112.0 0 NA 1 0 0 404 #> 405 25 0.0 0 NA 0 100.0 6 2 0 405 #> 406 25 24.0 0 NA 0 11.0 5 1 0 406 #> 407 25 24.0 0 NA 0 32.0 6 2 0 407 #> 408 25 36.0 0 NA 0 10.0 5 1 0 408 #> 409 25 36.0 0 NA 0 20.0 6 2 0 409 #> 410 25 48.0 0 NA 0 8.2 5 1 0 410 #> 411 25 48.0 0 NA 0 17.0 6 2 0 411 #> 412 25 72.0 0 NA 0 6.0 5 1 0 412 #> 413 25 72.0 0 NA 0 19.0 6 2 0 413 #> 414 25 96.0 0 NA 0 3.7 5 1 0 414 #> 415 25 96.0 0 NA 0 21.0 6 2 0 415 #> 416 25 120.0 0 NA 0 2.6 5 1 0 416 #> 417 25 120.0 0 NA 0 30.0 6 2 0 417 #> 418 26 0.0 1 120.0 0 NA 1 0 0 418 #> 419 26 0.0 0 NA 0 100.0 6 2 0 419 #> 420 26 24.0 0 NA 0 10.0 5 1 0 420 #> 421 26 24.0 0 NA 0 41.0 6 2 0 421 #> 422 26 36.0 0 NA 0 9.0 5 1 0 422 #> 423 26 36.0 0 NA 0 28.0 6 2 0 423 #> 424 26 48.0 0 NA 0 7.3 5 1 0 424 #> 425 26 48.0 0 NA 0 19.0 6 2 0 425 #> 426 26 72.0 0 NA 0 5.2 5 1 0 426 #> 427 26 72.0 0 NA 0 17.0 6 2 0 427 #> 428 26 96.0 0 NA 0 3.7 5 1 0 428 #> 429 26 96.0 0 NA 0 17.0 6 2 0 429 #> 430 26 120.0 0 NA 0 2.7 5 1 0 430 #> 431 26 120.0 0 NA 0 24.0 6 2 0 431 #> 432 27 0.0 1 120.0 0 NA 1 0 0 432 #> 433 27 0.0 0 NA 0 100.0 6 2 0 433 #> 434 27 24.0 0 NA 0 11.8 5 1 0 434 #> 435 27 24.0 0 NA 0 32.0 6 2 0 435 #> 436 27 36.0 0 NA 0 9.2 5 1 0 436 #> 437 27 36.0 0 NA 0 21.0 6 2 0 437 #> 438 27 48.0 0 NA 0 7.7 5 1 0 438 #> 439 27 48.0 0 NA 0 19.0 6 2 0 439 #> 440 27 72.0 0 NA 0 4.9 5 1 0 440 #> 441 27 72.0 0 NA 0 22.0 6 2 0 441 #> 442 27 96.0 0 NA 0 3.4 5 1 0 442 #> 443 27 96.0 0 NA 0 33.0 6 2 0 443 #> 444 27 120.0 0 NA 0 2.7 5 1 0 444 #> 445 27 120.0 0 NA 0 46.0 6 2 0 445 #> 446 28 0.0 1 120.0 0 NA 1 0 0 446 #> 447 28 0.0 0 NA 0 100.0 6 2 0 447 #> 448 28 24.0 0 NA 0 10.1 5 1 0 448 #> 449 28 24.0 0 NA 0 39.0 6 2 0 449 #> 450 28 36.0 0 NA 0 8.0 5 1 0 450 #> 451 28 36.0 0 NA 0 25.0 6 2 0 451 #> 452 28 48.0 0 NA 0 6.0 5 1 0 452 #> 453 28 48.0 0 NA 0 16.0 6 2 0 453 #> 454 28 72.0 0 NA 0 4.9 5 1 0 454 #> 455 28 72.0 0 NA 0 14.0 6 2 0 455 #> 456 28 96.0 0 NA 0 3.4 5 1 0 456 #> 457 28 96.0 0 NA 0 15.0 6 2 0 457 #> 458 28 120.0 0 NA 0 2.0 5 1 0 458 #> 459 28 120.0 0 NA 0 20.0 6 2 0 459 #> 460 29 0.0 1 153.0 0 NA 1 0 0 460 #> 461 29 0.0 0 NA 0 86.0 6 2 0 461 #> 462 29 24.0 0 NA 0 8.3 5 1 0 462 #> 463 29 24.0 0 NA 0 35.0 6 2 0 463 #> 464 29 36.0 0 NA 0 7.0 5 1 0 464 #> 465 29 36.0 0 NA 0 21.0 6 2 0 465 #> 466 29 48.0 0 NA 0 5.6 5 1 0 466 #> 467 29 48.0 0 NA 0 18.0 6 2 0 467 #> 468 29 72.0 0 NA 0 4.1 5 1 0 468 #> 469 29 72.0 0 NA 0 20.0 6 2 0 469 #> 470 29 96.0 0 NA 0 3.1 5 1 0 470 #> 471 29 96.0 0 NA 0 29.0 6 2 0 471 #> 472 29 120.0 0 NA 0 2.2 5 1 0 472 #> 473 29 120.0 0 NA 0 41.0 6 2 0 473 #> 474 30 0.0 1 105.0 0 NA 1 0 0 474 #> 475 30 0.0 0 NA 0 100.0 6 2 0 475 #> 476 30 24.0 0 NA 0 9.9 5 1 0 476 #> 477 30 24.0 0 NA 0 45.0 6 2 0 477 #> 478 30 36.0 0 NA 0 7.5 5 1 0 478 #> 479 30 36.0 0 NA 0 24.0 6 2 0 479 #> 480 30 48.0 0 NA 0 6.5 5 1 0 480 #> 481 30 48.0 0 NA 0 23.0 6 2 0 481 #> 482 30 72.0 0 NA 0 4.1 5 1 0 482 #> 483 30 72.0 0 NA 0 26.0 6 2 0 483 #> 484 30 96.0 0 NA 0 2.9 5 1 0 484 #> 485 30 96.0 0 NA 0 28.0 6 2 0 485 #> 486 30 120.0 0 NA 0 2.3 5 1 0 486 #> 487 30 120.0 0 NA 0 39.0 6 2 0 487 #> 488 31 0.0 1 125.0 0 NA 1 0 0 488 #> 489 31 0.0 0 NA 0 100.0 6 2 0 489 #> 490 31 24.0 0 NA 0 9.5 5 1 0 490 #> 491 31 24.0 0 NA 0 45.0 6 2 0 491 #> 492 31 36.0 0 NA 0 7.8 5 1 0 492 #> 493 31 36.0 0 NA 0 30.0 6 2 0 493 #> 494 31 48.0 0 NA 0 6.4 5 1 0 494 #> 495 31 48.0 0 NA 0 24.0 6 2 0 495 #> 496 31 72.0 0 NA 0 4.5 5 1 0 496 #> 497 31 72.0 0 NA 0 22.0 6 2 0 497 #> 498 31 96.0 0 NA 0 3.4 5 1 0 498 #> 499 31 96.0 0 NA 0 28.0 6 2 0 499 #> 500 31 120.0 0 NA 0 2.5 5 1 0 500 #> 501 31 120.0 0 NA 0 42.0 6 2 0 501 #> 502 32 0.0 1 93.0 0 NA 1 0 0 502 #> 503 32 0.0 0 NA 0 100.0 6 2 0 503 #> 504 32 24.0 0 NA 0 8.9 5 1 0 504 #> 505 32 24.0 0 NA 0 36.0 6 2 0 505 #> 506 32 36.0 0 NA 0 7.7 5 1 0 506 #> 507 32 36.0 0 NA 0 27.0 6 2 0 507 #> 508 32 48.0 0 NA 0 6.9 5 1 0 508 #> 509 32 48.0 0 NA 0 24.0 6 2 0 509 #> 510 32 72.0 0 NA 0 4.4 5 1 0 510 #> 511 32 72.0 0 NA 0 23.0 6 2 0 511 #> 512 32 96.0 0 NA 0 3.5 5 1 0 512 #> 513 32 96.0 0 NA 0 20.0 6 2 0 513 #> 514 32 120.0 0 NA 0 2.5 5 1 0 514 #> 515 32 120.0 0 NA 0 22.0 6 2 0 515 bblDatToRxode(pk.turnover.emax3, nlmixr2data::warfarin) #> #> #> ID TIME EVID AMT II DV CMT DVID SS nlmixrRowNums #> 1 1 0.0 1 100.0 0 NA 1 0 0 1 #> 2 1 0.5 0 NA 0 0.0 5 1 0 2 #> 3 1 1.0 0 NA 0 1.9 5 1 0 3 #> 4 1 2.0 0 NA 0 3.3 5 1 0 4 #> 5 1 3.0 0 NA 0 6.6 5 1 0 5 #> 6 1 6.0 0 NA 0 9.1 5 1 0 6 #> 7 1 9.0 0 NA 0 10.8 5 1 0 7 #> 8 1 12.0 0 NA 0 8.6 5 1 0 8 #> 9 1 24.0 0 NA 0 5.6 5 1 0 9 #> 10 1 24.0 0 NA 0 44.0 6 2 0 10 #> 11 1 36.0 0 NA 0 4.0 5 1 0 11 #> 12 1 36.0 0 NA 0 27.0 6 2 0 12 #> 13 1 48.0 0 NA 0 2.7 5 1 0 13 #> 14 1 48.0 0 NA 0 28.0 6 2 0 14 #> 15 1 72.0 0 NA 0 0.8 5 1 0 15 #> 16 1 72.0 0 NA 0 31.0 6 2 0 16 #> 17 1 96.0 0 NA 0 60.0 6 2 0 17 #> 18 1 120.0 0 NA 0 65.0 6 2 0 18 #> 19 1 144.0 0 NA 0 71.0 6 2 0 19 #> 20 2 0.0 1 100.0 0 NA 1 0 0 20 #> 21 2 0.0 0 NA 0 100.0 6 2 0 21 #> 22 2 24.0 0 NA 0 9.2 5 1 0 22 #> 23 2 24.0 0 NA 0 49.0 6 2 0 23 #> 24 2 36.0 0 NA 0 8.5 5 1 0 24 #> 25 2 36.0 0 NA 0 32.0 6 2 0 25 #> 26 2 48.0 0 NA 0 6.4 5 1 0 26 #> 27 2 48.0 0 NA 0 26.0 6 2 0 27 #> 28 2 72.0 0 NA 0 4.8 5 1 0 28 #> 29 2 72.0 0 NA 0 22.0 6 2 0 29 #> 30 2 96.0 0 NA 0 3.1 5 1 0 30 #> 31 2 96.0 0 NA 0 28.0 6 2 0 31 #> 32 2 120.0 0 NA 0 2.5 5 1 0 32 #> 33 2 120.0 0 NA 0 33.0 6 2 0 33 #> 34 3 0.0 1 100.0 0 NA 1 0 0 34 #> 35 3 0.0 0 NA 0 100.0 6 2 0 35 #> 36 3 0.5 0 NA 0 0.0 5 1 0 36 #> 37 3 2.0 0 NA 0 8.4 5 1 0 37 #> 38 3 3.0 0 NA 0 9.7 5 1 0 38 #> 39 3 6.0 0 NA 0 9.8 5 1 0 39 #> 40 3 12.0 0 NA 0 11.0 5 1 0 40 #> 41 3 24.0 0 NA 0 8.3 5 1 0 41 #> 42 3 24.0 0 NA 0 46.0 6 2 0 42 #> 43 3 36.0 0 NA 0 7.7 5 1 0 43 #> 44 3 36.0 0 NA 0 22.0 6 2 0 44 #> 45 3 48.0 0 NA 0 6.3 5 1 0 45 #> 46 3 48.0 0 NA 0 19.0 6 2 0 46 #> 47 3 72.0 0 NA 0 4.1 5 1 0 47 #> 48 3 72.0 0 NA 0 20.0 6 2 0 48 #> 49 3 96.0 0 NA 0 3.0 5 1 0 49 #> 50 3 96.0 0 NA 0 42.0 6 2 0 50 #> 51 3 120.0 0 NA 0 1.4 5 1 0 51 #> 52 3 120.0 0 NA 0 49.0 6 2 0 52 #> 53 3 144.0 0 NA 0 54.0 6 2 0 53 #> 54 4 0.0 1 120.0 0 NA 1 0 0 54 #> 55 4 0.0 0 NA 0 100.0 6 2 0 55 #> 56 4 3.0 0 NA 0 12.0 5 1 0 56 #> 57 4 6.0 0 NA 0 13.2 5 1 0 57 #> 58 4 9.0 0 NA 0 14.4 5 1 0 58 #> 59 4 24.0 0 NA 0 9.6 5 1 0 59 #> 60 4 24.0 0 NA 0 30.0 6 2 0 60 #> 61 4 36.0 0 NA 0 8.2 5 1 0 61 #> 62 4 36.0 0 NA 0 24.0 6 2 0 62 #> 63 4 48.0 0 NA 0 7.8 5 1 0 63 #> 64 4 48.0 0 NA 0 13.0 6 2 0 64 #> 65 4 72.0 0 NA 0 5.8 5 1 0 65 #> 66 4 72.0 0 NA 0 9.0 6 2 0 66 #> 67 4 96.0 0 NA 0 4.3 5 1 0 67 #> 68 4 96.0 0 NA 0 9.0 6 2 0 68 #> 69 4 120.0 0 NA 0 3.0 5 1 0 69 #> 70 4 120.0 0 NA 0 11.0 6 2 0 70 #> 71 4 144.0 0 NA 0 12.0 6 2 0 71 #> 72 5 0.0 1 60.0 0 NA 1 0 0 72 #> 73 5 0.0 0 NA 0 82.0 6 2 0 73 #> 74 5 3.0 0 NA 0 11.1 5 1 0 74 #> 75 5 6.0 0 NA 0 11.9 5 1 0 75 #> 76 5 9.0 0 NA 0 9.8 5 1 0 76 #> 77 5 12.0 0 NA 0 11.0 5 1 0 77 #> 78 5 24.0 0 NA 0 8.5 5 1 0 78 #> 79 5 24.0 0 NA 0 43.0 6 2 0 79 #> 80 5 36.0 0 NA 0 7.6 5 1 0 80 #> 81 5 36.0 0 NA 0 25.0 6 2 0 81 #> 82 5 48.0 0 NA 0 5.4 5 1 0 82 #> 83 5 48.0 0 NA 0 18.0 6 2 0 83 #> 84 5 72.0 0 NA 0 4.5 5 1 0 84 #> 85 5 72.0 0 NA 0 17.0 6 2 0 85 #> 86 5 96.0 0 NA 0 3.3 5 1 0 86 #> 87 5 96.0 0 NA 0 23.0 6 2 0 87 #> 88 5 120.0 0 NA 0 2.3 5 1 0 88 #> 89 5 120.0 0 NA 0 29.0 6 2 0 89 #> 90 5 144.0 0 NA 0 41.0 6 2 0 90 #> 91 6 0.0 1 113.0 0 NA 1 0 0 91 #> 92 6 0.0 0 NA 0 100.0 6 2 0 92 #> 93 6 6.0 0 NA 0 8.6 5 1 0 93 #> 94 6 12.0 0 NA 0 8.6 5 1 0 94 #> 95 6 24.0 0 NA 0 7.0 5 1 0 95 #> 96 6 24.0 0 NA 0 34.0 6 2 0 96 #> 97 6 36.0 0 NA 0 5.7 5 1 0 97 #> 98 6 36.0 0 NA 0 23.0 6 2 0 98 #> 99 6 48.0 0 NA 0 4.7 5 1 0 99 #> 100 6 48.0 0 NA 0 20.0 6 2 0 100 #> 101 6 72.0 0 NA 0 3.3 5 1 0 101 #> 102 6 72.0 0 NA 0 16.0 6 2 0 102 #> 103 6 96.0 0 NA 0 2.3 5 1 0 103 #> 104 6 96.0 0 NA 0 17.0 6 2 0 104 #> 105 6 120.0 0 NA 0 1.7 5 1 0 105 #> 106 6 120.0 0 NA 0 18.0 6 2 0 106 #> 107 6 144.0 0 NA 0 25.0 6 2 0 107 #> 108 7 0.0 1 90.0 0 NA 1 0 0 108 #> 109 7 3.0 0 NA 0 13.4 5 1 0 109 #> 110 7 6.0 0 NA 0 12.4 5 1 0 110 #> 111 7 9.0 0 NA 0 12.7 5 1 0 111 #> 112 7 12.0 0 NA 0 8.8 5 1 0 112 #> 113 7 24.0 0 NA 0 6.1 5 1 0 113 #> 114 7 24.0 0 NA 0 36.0 6 2 0 114 #> 115 7 36.0 0 NA 0 3.5 5 1 0 115 #> 116 7 36.0 0 NA 0 33.0 6 2 0 116 #> 117 7 48.0 0 NA 0 1.8 5 1 0 117 #> 118 7 48.0 0 NA 0 28.0 6 2 0 118 #> 119 7 72.0 0 NA 0 1.5 5 1 0 119 #> 120 7 72.0 0 NA 0 52.0 6 2 0 120 #> 121 7 96.0 0 NA 0 1.0 5 1 0 121 #> 122 7 96.0 0 NA 0 80.0 6 2 0 122 #> 123 7 120.0 0 NA 0 90.0 6 2 0 123 #> 124 7 144.0 0 NA 0 100.0 6 2 0 124 #> 125 8 0.0 1 135.0 0 NA 1 0 0 125 #> 126 8 0.0 0 NA 0 88.0 6 2 0 126 #> 127 8 2.0 0 NA 0 17.6 5 1 0 127 #> 128 8 3.0 0 NA 0 17.3 5 1 0 128 #> 129 8 6.0 0 NA 0 15.0 5 1 0 129 #> 130 8 9.0 0 NA 0 15.0 5 1 0 130 #> 131 8 12.0 0 NA 0 12.4 5 1 0 131 #> 132 8 24.0 0 NA 0 7.9 5 1 0 132 #> 133 8 24.0 0 NA 0 35.0 6 2 0 133 #> 134 8 36.0 0 NA 0 7.9 5 1 0 134 #> 135 8 36.0 0 NA 0 20.0 6 2 0 135 #> 136 8 48.0 0 NA 0 5.1 5 1 0 136 #> 137 8 48.0 0 NA 0 12.0 6 2 0 137 #> 138 8 72.0 0 NA 0 3.6 5 1 0 138 #> 139 8 72.0 0 NA 0 16.0 6 2 0 139 #> 140 8 96.0 0 NA 0 2.4 5 1 0 140 #> 141 8 96.0 0 NA 0 23.0 6 2 0 141 #> 142 8 120.0 0 NA 0 2.0 5 1 0 142 #> 143 8 120.0 0 NA 0 36.0 6 2 0 143 #> 144 8 144.0 0 NA 0 48.0 6 2 0 144 #> 145 9 0.0 1 75.0 0 NA 1 0 0 145 #> 146 9 0.0 0 NA 0 92.0 6 2 0 146 #> 147 9 0.5 0 NA 0 0.0 5 1 0 147 #> 148 9 1.0 0 NA 0 1.0 5 1 0 148 #> 149 9 2.0 0 NA 0 4.6 5 1 0 149 #> 150 9 3.0 0 NA 0 12.7 5 1 0 150 #> 151 9 3.0 0 NA 0 8.0 5 1 0 151 #> 152 9 6.0 0 NA 0 12.7 5 1 0 152 #> 153 9 6.0 0 NA 0 11.5 5 1 0 153 #> 154 9 9.0 0 NA 0 12.9 5 1 0 154 #> 155 9 9.0 0 NA 0 11.4 5 1 0 155 #> 156 9 12.0 0 NA 0 11.4 5 1 0 156 #> 157 9 12.0 0 NA 0 11.0 5 1 0 157 #> 158 9 24.0 0 NA 0 9.1 5 1 0 158 #> 159 9 24.0 0 NA 0 33.0 6 2 0 159 #> 160 9 36.0 0 NA 0 8.2 5 1 0 160 #> 161 9 36.0 0 NA 0 22.0 6 2 0 161 #> 162 9 48.0 0 NA 0 5.9 5 1 0 162 #> 163 9 48.0 0 NA 0 16.0 6 2 0 163 #> 164 9 72.0 0 NA 0 3.6 5 1 0 164 #> 165 9 72.0 0 NA 0 18.0 6 2 0 165 #> 166 9 96.0 0 NA 0 1.7 5 1 0 166 #> 167 9 96.0 0 NA 0 32.0 6 2 0 167 #> 168 9 120.0 0 NA 0 1.1 5 1 0 168 #> 169 9 120.0 0 NA 0 30.0 6 2 0 169 #> 170 9 144.0 0 NA 0 45.0 6 2 0 170 #> 171 10 0.0 1 105.0 0 NA 1 0 0 171 #> 172 10 0.0 0 NA 0 90.0 6 2 0 172 #> 173 10 24.0 0 NA 0 8.6 5 1 0 173 #> 174 10 24.0 0 NA 0 39.0 6 2 0 174 #> 175 10 36.0 0 NA 0 8.0 5 1 0 175 #> 176 10 36.0 0 NA 0 22.0 6 2 0 176 #> 177 10 48.0 0 NA 0 6.0 5 1 0 177 #> 178 10 48.0 0 NA 0 17.0 6 2 0 178 #> 179 10 72.0 0 NA 0 4.4 5 1 0 179 #> 180 10 72.0 0 NA 0 17.0 6 2 0 180 #> 181 10 96.0 0 NA 0 3.6 5 1 0 181 #> 182 10 96.0 0 NA 0 22.0 6 2 0 182 #> 183 10 120.0 0 NA 0 2.8 5 1 0 183 #> 184 10 120.0 0 NA 0 25.0 6 2 0 184 #> 185 10 144.0 0 NA 0 33.0 6 2 0 185 #> 186 11 0.0 1 123.0 0 NA 1 0 0 186 #> 187 11 0.0 0 NA 0 100.0 6 2 0 187 #> 188 11 1.5 0 NA 0 11.4 5 1 0 188 #> 189 11 3.0 0 NA 0 15.4 5 1 0 189 #> 190 11 6.0 0 NA 0 17.5 5 1 0 190 #> 191 11 12.0 0 NA 0 14.0 5 1 0 191 #> 192 11 24.0 0 NA 0 9.0 5 1 0 192 #> 193 11 24.0 0 NA 0 37.0 6 2 0 193 #> 194 11 36.0 0 NA 0 8.9 5 1 0 194 #> 195 11 36.0 0 NA 0 24.0 6 2 0 195 #> 196 11 48.0 0 NA 0 6.6 5 1 0 196 #> 197 11 48.0 0 NA 0 14.0 6 2 0 197 #> 198 11 72.0 0 NA 0 4.2 5 1 0 198 #> 199 11 72.0 0 NA 0 11.0 6 2 0 199 #> 200 11 96.0 0 NA 0 3.6 5 1 0 200 #> 201 11 96.0 0 NA 0 14.0 6 2 0 201 #> 202 11 120.0 0 NA 0 2.6 5 1 0 202 #> 203 11 120.0 0 NA 0 23.0 6 2 0 203 #> 204 11 144.0 0 NA 0 33.0 6 2 0 204 #> 205 12 0.0 1 113.0 0 NA 1 0 0 205 #> 206 12 0.0 0 NA 0 85.0 6 2 0 206 #> 207 12 1.5 0 NA 0 0.6 5 1 0 207 #> 208 12 3.0 0 NA 0 2.8 5 1 0 208 #> 209 12 6.0 0 NA 0 13.8 5 1 0 209 #> 210 12 9.0 0 NA 0 15.0 5 1 0 210 #> 211 12 24.0 0 NA 0 10.5 5 1 0 211 #> 212 12 24.0 0 NA 0 25.0 6 2 0 212 #> 213 12 36.0 0 NA 0 9.1 5 1 0 213 #> 214 12 36.0 0 NA 0 15.0 6 2 0 214 #> 215 12 48.0 0 NA 0 6.6 5 1 0 215 #> 216 12 48.0 0 NA 0 11.0 6 2 0 216 #> 217 12 72.0 0 NA 0 4.9 5 1 0 217 #> 218 12 96.0 0 NA 0 2.4 5 1 0 218 #> 219 12 120.0 0 NA 0 1.9 5 1 0 219 #> 220 13 0.0 1 113.0 0 NA 1 0 0 220 #> 221 13 0.0 0 NA 0 88.0 6 2 0 221 #> 222 13 1.5 0 NA 0 3.6 5 1 0 222 #> 223 13 3.0 0 NA 0 12.9 5 1 0 223 #> 224 13 6.0 0 NA 0 12.9 5 1 0 224 #> 225 13 9.0 0 NA 0 10.2 5 1 0 225 #> 226 13 24.0 0 NA 0 6.4 5 1 0 226 #> 227 13 24.0 0 NA 0 41.0 6 2 0 227 #> 228 13 36.0 0 NA 0 6.9 5 1 0 228 #> 229 13 36.0 0 NA 0 23.0 6 2 0 229 #> 230 13 48.0 0 NA 0 4.5 5 1 0 230 #> 231 13 48.0 0 NA 0 16.0 6 2 0 231 #> 232 13 72.0 0 NA 0 3.2 5 1 0 232 #> 233 13 72.0 0 NA 0 14.0 6 2 0 233 #> 234 13 96.0 0 NA 0 2.4 5 1 0 234 #> 235 13 96.0 0 NA 0 18.0 6 2 0 235 #> 236 13 120.0 0 NA 0 1.3 5 1 0 236 #> 237 13 120.0 0 NA 0 22.0 6 2 0 237 #> 238 13 144.0 0 NA 0 35.0 6 2 0 238 #> 239 14 0.0 1 75.0 0 NA 1 0 0 239 #> 240 14 0.0 0 NA 0 85.0 6 2 0 240 #> 241 14 0.5 0 NA 0 0.0 5 1 0 241 #> 242 14 1.0 0 NA 0 2.7 5 1 0 242 #> 243 14 2.0 0 NA 0 11.6 5 1 0 243 #> 244 14 3.0 0 NA 0 11.6 5 1 0 244 #> 245 14 6.0 0 NA 0 11.3 5 1 0 245 #> 246 14 9.0 0 NA 0 9.7 5 1 0 246 #> 247 14 24.0 0 NA 0 6.5 5 1 0 247 #> 248 14 24.0 0 NA 0 32.0 6 2 0 248 #> 249 14 36.0 0 NA 0 5.2 5 1 0 249 #> 250 14 36.0 0 NA 0 22.0 6 2 0 250 #> 251 14 48.0 0 NA 0 3.6 5 1 0 251 #> 252 14 48.0 0 NA 0 21.0 6 2 0 252 #> 253 14 72.0 0 NA 0 2.4 5 1 0 253 #> 254 14 72.0 0 NA 0 28.0 6 2 0 254 #> 255 14 96.0 0 NA 0 0.9 5 1 0 255 #> 256 14 96.0 0 NA 0 38.0 6 2 0 256 #> 257 14 120.0 0 NA 0 46.0 6 2 0 257 #> 258 14 144.0 0 NA 0 65.0 6 2 0 258 #> 259 15 0.0 1 85.0 0 NA 1 0 0 259 #> 260 15 0.0 0 NA 0 100.0 6 2 0 260 #> 261 15 1.0 0 NA 0 6.6 5 1 0 261 #> 262 15 3.0 0 NA 0 11.9 5 1 0 262 #> 263 15 6.0 0 NA 0 11.7 5 1 0 263 #> 264 15 9.0 0 NA 0 12.2 5 1 0 264 #> 265 15 24.0 0 NA 0 8.1 5 1 0 265 #> 266 15 24.0 0 NA 0 43.0 6 2 0 266 #> 267 15 36.0 0 NA 0 7.4 5 1 0 267 #> 268 15 36.0 0 NA 0 26.0 6 2 0 268 #> 269 15 48.0 0 NA 0 6.8 5 1 0 269 #> 270 15 48.0 0 NA 0 15.0 6 2 0 270 #> 271 15 72.0 0 NA 0 5.3 5 1 0 271 #> 272 15 72.0 0 NA 0 13.0 6 2 0 272 #> 273 15 96.0 0 NA 0 3.0 5 1 0 273 #> 274 15 96.0 0 NA 0 21.0 6 2 0 274 #> 275 15 120.0 0 NA 0 2.0 5 1 0 275 #> 276 15 120.0 0 NA 0 28.0 6 2 0 276 #> 277 15 144.0 0 NA 0 39.0 6 2 0 277 #> 278 16 0.0 1 87.0 0 NA 1 0 0 278 #> 279 16 0.0 0 NA 0 100.0 6 2 0 279 #> 280 16 24.0 0 NA 0 10.4 5 1 0 280 #> 281 16 24.0 0 NA 0 42.0 6 2 0 281 #> 282 16 36.0 0 NA 0 8.9 5 1 0 282 #> 283 16 36.0 0 NA 0 32.0 6 2 0 283 #> 284 16 48.0 0 NA 0 7.0 5 1 0 284 #> 285 16 48.0 0 NA 0 26.0 6 2 0 285 #> 286 16 72.0 0 NA 0 4.4 5 1 0 286 #> 287 16 72.0 0 NA 0 31.0 6 2 0 287 #> 288 16 96.0 0 NA 0 3.2 5 1 0 288 #> 289 16 96.0 0 NA 0 33.0 6 2 0 289 #> 290 16 120.0 0 NA 0 2.4 5 1 0 290 #> 291 16 120.0 0 NA 0 54.0 6 2 0 291 #> 292 17 0.0 1 117.0 0 NA 1 0 0 292 #> 293 17 0.0 0 NA 0 100.0 6 2 0 293 #> 294 17 24.0 0 NA 0 7.6 5 1 0 294 #> 295 17 24.0 0 NA 0 35.0 6 2 0 295 #> 296 17 36.0 0 NA 0 6.4 5 1 0 296 #> 297 17 36.0 0 NA 0 23.0 6 2 0 297 #> 298 17 48.0 0 NA 0 6.0 5 1 0 298 #> 299 17 48.0 0 NA 0 17.0 6 2 0 299 #> 300 17 72.0 0 NA 0 4.0 5 1 0 300 #> 301 17 72.0 0 NA 0 18.0 6 2 0 301 #> 302 17 96.0 0 NA 0 3.1 5 1 0 302 #> 303 17 96.0 0 NA 0 18.0 6 2 0 303 #> 304 17 120.0 0 NA 0 2.0 5 1 0 304 #> 305 17 120.0 0 NA 0 21.0 6 2 0 305 #> 306 18 0.0 1 112.0 0 NA 1 0 0 306 #> 307 18 0.0 0 NA 0 100.0 6 2 0 307 #> 308 18 24.0 0 NA 0 7.6 5 1 0 308 #> 309 18 24.0 0 NA 0 32.0 6 2 0 309 #> 310 18 36.0 0 NA 0 6.6 5 1 0 310 #> 311 18 36.0 0 NA 0 20.0 6 2 0 311 #> 312 18 48.0 0 NA 0 5.4 5 1 0 312 #> 313 18 48.0 0 NA 0 18.0 6 2 0 313 #> 314 18 72.0 0 NA 0 3.4 5 1 0 314 #> 315 18 72.0 0 NA 0 18.0 6 2 0 315 #> 316 18 96.0 0 NA 0 1.2 5 1 0 316 #> 317 18 96.0 0 NA 0 19.0 6 2 0 317 #> 318 18 120.0 0 NA 0 0.9 5 1 0 318 #> 319 18 120.0 0 NA 0 29.0 6 2 0 319 #> 320 19 0.0 1 95.5 0 NA 1 0 0 320 #> 321 19 0.0 0 NA 0 100.0 6 2 0 321 #> 322 19 24.0 0 NA 0 6.6 5 1 0 322 #> 323 19 24.0 0 NA 0 33.0 6 2 0 323 #> 324 19 36.0 0 NA 0 5.3 5 1 0 324 #> 325 19 36.0 0 NA 0 28.0 6 2 0 325 #> 326 19 48.0 0 NA 0 3.6 5 1 0 326 #> 327 19 48.0 0 NA 0 18.0 6 2 0 327 #> 328 19 72.0 0 NA 0 2.7 5 1 0 328 #> 329 19 72.0 0 NA 0 18.0 6 2 0 329 #> 330 19 96.0 0 NA 0 1.4 5 1 0 330 #> 331 19 96.0 0 NA 0 17.0 6 2 0 331 #> 332 19 120.0 0 NA 0 1.1 5 1 0 332 #> 333 19 120.0 0 NA 0 26.0 6 2 0 333 #> 334 20 0.0 1 88.5 0 NA 1 0 0 334 #> 335 20 0.0 0 NA 0 100.0 6 2 0 335 #> 336 20 24.0 0 NA 0 9.6 5 1 0 336 #> 337 20 24.0 0 NA 0 41.0 6 2 0 337 #> 338 20 36.0 0 NA 0 8.0 5 1 0 338 #> 339 20 36.0 0 NA 0 30.0 6 2 0 339 #> 340 20 48.0 0 NA 0 6.6 5 1 0 340 #> 341 20 48.0 0 NA 0 22.0 6 2 0 341 #> 342 20 72.0 0 NA 0 5.6 5 1 0 342 #> 343 20 72.0 0 NA 0 23.0 6 2 0 343 #> 344 20 96.0 0 NA 0 3.5 5 1 0 344 #> 345 20 96.0 0 NA 0 23.0 6 2 0 345 #> 346 20 120.0 0 NA 0 2.3 5 1 0 346 #> 347 20 120.0 0 NA 0 35.0 6 2 0 347 #> 348 21 0.0 1 93.0 0 NA 1 0 0 348 #> 349 21 0.0 0 NA 0 100.0 6 2 0 349 #> 350 21 24.0 0 NA 0 7.3 5 1 0 350 #> 351 21 24.0 0 NA 0 46.0 6 2 0 351 #> 352 21 36.0 0 NA 0 6.1 5 1 0 352 #> 353 21 36.0 0 NA 0 27.0 6 2 0 353 #> 354 21 48.0 0 NA 0 4.3 5 1 0 354 #> 355 21 48.0 0 NA 0 22.0 6 2 0 355 #> 356 21 72.0 0 NA 0 3.2 5 1 0 356 #> 357 21 72.0 0 NA 0 36.0 6 2 0 357 #> 358 21 96.0 0 NA 0 2.3 5 1 0 358 #> 359 21 96.0 0 NA 0 40.0 6 2 0 359 #> 360 21 120.0 0 NA 0 1.9 5 1 0 360 #> 361 21 120.0 0 NA 0 44.0 6 2 0 361 #> 362 22 0.0 1 87.0 0 NA 1 0 0 362 #> 363 22 0.0 0 NA 0 100.0 6 2 0 363 #> 364 22 24.0 0 NA 0 8.9 5 1 0 364 #> 365 22 24.0 0 NA 0 35.0 6 2 0 365 #> 366 22 36.0 0 NA 0 8.4 5 1 0 366 #> 367 22 36.0 0 NA 0 27.0 6 2 0 367 #> 368 22 48.0 0 NA 0 8.0 5 1 0 368 #> 369 22 48.0 0 NA 0 23.0 6 2 0 369 #> 370 22 72.0 0 NA 0 4.4 5 1 0 370 #> 371 22 72.0 0 NA 0 27.0 6 2 0 371 #> 372 22 96.0 0 NA 0 3.2 5 1 0 372 #> 373 22 96.0 0 NA 0 43.0 6 2 0 373 #> 374 22 120.0 0 NA 0 1.7 5 1 0 374 #> 375 22 120.0 0 NA 0 43.0 6 2 0 375 #> 376 23 0.0 1 110.0 0 NA 1 0 0 376 #> 377 23 0.0 0 NA 0 100.0 6 2 0 377 #> 378 23 24.0 0 NA 0 9.8 5 1 0 378 #> 379 23 24.0 0 NA 0 34.0 6 2 0 379 #> 380 23 36.0 0 NA 0 8.4 5 1 0 380 #> 381 23 36.0 0 NA 0 24.0 6 2 0 381 #> 382 23 48.0 0 NA 0 6.6 5 1 0 382 #> 383 23 48.0 0 NA 0 15.0 6 2 0 383 #> 384 23 72.0 0 NA 0 4.8 5 1 0 384 #> 385 23 72.0 0 NA 0 15.0 6 2 0 385 #> 386 23 96.0 0 NA 0 3.2 5 1 0 386 #> 387 23 96.0 0 NA 0 19.0 6 2 0 387 #> 388 23 120.0 0 NA 0 2.4 5 1 0 388 #> 389 23 120.0 0 NA 0 19.0 6 2 0 389 #> 390 24 0.0 1 115.0 0 NA 1 0 0 390 #> 391 24 0.0 0 NA 0 88.0 6 2 0 391 #> 392 24 24.0 0 NA 0 8.2 5 1 0 392 #> 393 24 24.0 0 NA 0 37.0 6 2 0 393 #> 394 24 36.0 0 NA 0 7.5 5 1 0 394 #> 395 24 36.0 0 NA 0 20.0 6 2 0 395 #> 396 24 48.0 0 NA 0 6.8 5 1 0 396 #> 397 24 48.0 0 NA 0 20.0 6 2 0 397 #> 398 24 72.0 0 NA 0 5.5 5 1 0 398 #> 399 24 72.0 0 NA 0 26.0 6 2 0 399 #> 400 24 96.0 0 NA 0 4.5 5 1 0 400 #> 401 24 96.0 0 NA 0 28.0 6 2 0 401 #> 402 24 120.0 0 NA 0 3.7 5 1 0 402 #> 403 24 120.0 0 NA 0 50.0 6 2 0 403 #> 404 25 0.0 1 112.0 0 NA 1 0 0 404 #> 405 25 0.0 0 NA 0 100.0 6 2 0 405 #> 406 25 24.0 0 NA 0 11.0 5 1 0 406 #> 407 25 24.0 0 NA 0 32.0 6 2 0 407 #> 408 25 36.0 0 NA 0 10.0 5 1 0 408 #> 409 25 36.0 0 NA 0 20.0 6 2 0 409 #> 410 25 48.0 0 NA 0 8.2 5 1 0 410 #> 411 25 48.0 0 NA 0 17.0 6 2 0 411 #> 412 25 72.0 0 NA 0 6.0 5 1 0 412 #> 413 25 72.0 0 NA 0 19.0 6 2 0 413 #> 414 25 96.0 0 NA 0 3.7 5 1 0 414 #> 415 25 96.0 0 NA 0 21.0 6 2 0 415 #> 416 25 120.0 0 NA 0 2.6 5 1 0 416 #> 417 25 120.0 0 NA 0 30.0 6 2 0 417 #> 418 26 0.0 1 120.0 0 NA 1 0 0 418 #> 419 26 0.0 0 NA 0 100.0 6 2 0 419 #> 420 26 24.0 0 NA 0 10.0 5 1 0 420 #> 421 26 24.0 0 NA 0 41.0 6 2 0 421 #> 422 26 36.0 0 NA 0 9.0 5 1 0 422 #> 423 26 36.0 0 NA 0 28.0 6 2 0 423 #> 424 26 48.0 0 NA 0 7.3 5 1 0 424 #> 425 26 48.0 0 NA 0 19.0 6 2 0 425 #> 426 26 72.0 0 NA 0 5.2 5 1 0 426 #> 427 26 72.0 0 NA 0 17.0 6 2 0 427 #> 428 26 96.0 0 NA 0 3.7 5 1 0 428 #> 429 26 96.0 0 NA 0 17.0 6 2 0 429 #> 430 26 120.0 0 NA 0 2.7 5 1 0 430 #> 431 26 120.0 0 NA 0 24.0 6 2 0 431 #> 432 27 0.0 1 120.0 0 NA 1 0 0 432 #> 433 27 0.0 0 NA 0 100.0 6 2 0 433 #> 434 27 24.0 0 NA 0 11.8 5 1 0 434 #> 435 27 24.0 0 NA 0 32.0 6 2 0 435 #> 436 27 36.0 0 NA 0 9.2 5 1 0 436 #> 437 27 36.0 0 NA 0 21.0 6 2 0 437 #> 438 27 48.0 0 NA 0 7.7 5 1 0 438 #> 439 27 48.0 0 NA 0 19.0 6 2 0 439 #> 440 27 72.0 0 NA 0 4.9 5 1 0 440 #> 441 27 72.0 0 NA 0 22.0 6 2 0 441 #> 442 27 96.0 0 NA 0 3.4 5 1 0 442 #> 443 27 96.0 0 NA 0 33.0 6 2 0 443 #> 444 27 120.0 0 NA 0 2.7 5 1 0 444 #> 445 27 120.0 0 NA 0 46.0 6 2 0 445 #> 446 28 0.0 1 120.0 0 NA 1 0 0 446 #> 447 28 0.0 0 NA 0 100.0 6 2 0 447 #> 448 28 24.0 0 NA 0 10.1 5 1 0 448 #> 449 28 24.0 0 NA 0 39.0 6 2 0 449 #> 450 28 36.0 0 NA 0 8.0 5 1 0 450 #> 451 28 36.0 0 NA 0 25.0 6 2 0 451 #> 452 28 48.0 0 NA 0 6.0 5 1 0 452 #> 453 28 48.0 0 NA 0 16.0 6 2 0 453 #> 454 28 72.0 0 NA 0 4.9 5 1 0 454 #> 455 28 72.0 0 NA 0 14.0 6 2 0 455 #> 456 28 96.0 0 NA 0 3.4 5 1 0 456 #> 457 28 96.0 0 NA 0 15.0 6 2 0 457 #> 458 28 120.0 0 NA 0 2.0 5 1 0 458 #> 459 28 120.0 0 NA 0 20.0 6 2 0 459 #> 460 29 0.0 1 153.0 0 NA 1 0 0 460 #> 461 29 0.0 0 NA 0 86.0 6 2 0 461 #> 462 29 24.0 0 NA 0 8.3 5 1 0 462 #> 463 29 24.0 0 NA 0 35.0 6 2 0 463 #> 464 29 36.0 0 NA 0 7.0 5 1 0 464 #> 465 29 36.0 0 NA 0 21.0 6 2 0 465 #> 466 29 48.0 0 NA 0 5.6 5 1 0 466 #> 467 29 48.0 0 NA 0 18.0 6 2 0 467 #> 468 29 72.0 0 NA 0 4.1 5 1 0 468 #> 469 29 72.0 0 NA 0 20.0 6 2 0 469 #> 470 29 96.0 0 NA 0 3.1 5 1 0 470 #> 471 29 96.0 0 NA 0 29.0 6 2 0 471 #> 472 29 120.0 0 NA 0 2.2 5 1 0 472 #> 473 29 120.0 0 NA 0 41.0 6 2 0 473 #> 474 30 0.0 1 105.0 0 NA 1 0 0 474 #> 475 30 0.0 0 NA 0 100.0 6 2 0 475 #> 476 30 24.0 0 NA 0 9.9 5 1 0 476 #> 477 30 24.0 0 NA 0 45.0 6 2 0 477 #> 478 30 36.0 0 NA 0 7.5 5 1 0 478 #> 479 30 36.0 0 NA 0 24.0 6 2 0 479 #> 480 30 48.0 0 NA 0 6.5 5 1 0 480 #> 481 30 48.0 0 NA 0 23.0 6 2 0 481 #> 482 30 72.0 0 NA 0 4.1 5 1 0 482 #> 483 30 72.0 0 NA 0 26.0 6 2 0 483 #> 484 30 96.0 0 NA 0 2.9 5 1 0 484 #> 485 30 96.0 0 NA 0 28.0 6 2 0 485 #> 486 30 120.0 0 NA 0 2.3 5 1 0 486 #> 487 30 120.0 0 NA 0 39.0 6 2 0 487 #> 488 31 0.0 1 125.0 0 NA 1 0 0 488 #> 489 31 0.0 0 NA 0 100.0 6 2 0 489 #> 490 31 24.0 0 NA 0 9.5 5 1 0 490 #> 491 31 24.0 0 NA 0 45.0 6 2 0 491 #> 492 31 36.0 0 NA 0 7.8 5 1 0 492 #> 493 31 36.0 0 NA 0 30.0 6 2 0 493 #> 494 31 48.0 0 NA 0 6.4 5 1 0 494 #> 495 31 48.0 0 NA 0 24.0 6 2 0 495 #> 496 31 72.0 0 NA 0 4.5 5 1 0 496 #> 497 31 72.0 0 NA 0 22.0 6 2 0 497 #> 498 31 96.0 0 NA 0 3.4 5 1 0 498 #> 499 31 96.0 0 NA 0 28.0 6 2 0 499 #> 500 31 120.0 0 NA 0 2.5 5 1 0 500 #> 501 31 120.0 0 NA 0 42.0 6 2 0 501 #> 502 32 0.0 1 93.0 0 NA 1 0 0 502 #> 503 32 0.0 0 NA 0 100.0 6 2 0 503 #> 504 32 24.0 0 NA 0 8.9 5 1 0 504 #> 505 32 24.0 0 NA 0 36.0 6 2 0 505 #> 506 32 36.0 0 NA 0 7.7 5 1 0 506 #> 507 32 36.0 0 NA 0 27.0 6 2 0 507 #> 508 32 48.0 0 NA 0 6.9 5 1 0 508 #> 509 32 48.0 0 NA 0 24.0 6 2 0 509 #> 510 32 72.0 0 NA 0 4.4 5 1 0 510 #> 511 32 72.0 0 NA 0 23.0 6 2 0 511 #> 512 32 96.0 0 NA 0 3.5 5 1 0 512 #> 513 32 96.0 0 NA 0 20.0 6 2 0 513 #> 514 32 120.0 0 NA 0 2.5 5 1 0 514 #> 515 32 120.0 0 NA 0 22.0 6 2 0 515"},{"path":"/reference/getStandardColNames.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine standardized rxode2 column names from data — getStandardColNames","title":"Determine standardized rxode2 column names from data — getStandardColNames","text":"Determine standardized rxode2 column names data","code":""},{"path":"/reference/getStandardColNames.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine standardized rxode2 column names from data — getStandardColNames","text":"","code":"getStandardColNames(data)"},{"path":"/reference/getStandardColNames.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine standardized rxode2 column names from data — getStandardColNames","text":"data data.frame source column names","code":""},{"path":"/reference/getStandardColNames.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine standardized rxode2 column names from data — getStandardColNames","text":"named character vector names standardized names values either name column data NA column present data.","code":""},{"path":"/reference/getStandardColNames.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine standardized rxode2 column names from data — getStandardColNames","text":"","code":"getStandardColNames(data.frame(ID=1, DV=2, Time=3, CmT=4)) #> id time amt rate dur evid cmt ss ii addl dv #> \"ID\" \"Time\" NA NA NA NA \"CmT\" NA NA NA \"DV\" #> mdv dvid cens limit #> NA NA NA NA"},{"path":"/reference/modelUnitConversion.html","id":null,"dir":"Reference","previous_headings":"","what":"Unit conversion for pharmacokinetic models — modelUnitConversion","title":"Unit conversion for pharmacokinetic models — modelUnitConversion","text":"Unit conversion pharmacokinetic models","code":""},{"path":"/reference/modelUnitConversion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unit conversion for pharmacokinetic models — modelUnitConversion","text":"","code":"modelUnitConversion( dvu = NA_character_, amtu = NA_character_, timeu = NA_character_, volumeu = NA_character_ )"},{"path":"/reference/modelUnitConversion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unit conversion for pharmacokinetic models — modelUnitConversion","text":"dvu, amtu, timeu units DV, AMT, TIME columns data volumeu units volume parameters model","code":""},{"path":"/reference/modelUnitConversion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Unit conversion for pharmacokinetic models — modelUnitConversion","text":"list names units associated parameter (\"amtu\", \"clearanceu\", \"volumeu\", \"timeu\", \"dvu\") numeric value multiply modeled estimate (example, cp) model consistent data units.","code":""},{"path":[]},{"path":"/reference/modelUnitConversion.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unit conversion for pharmacokinetic models — modelUnitConversion","text":"","code":"modelUnitConversion(dvu = \"ng/mL\", amtu = \"mg\", timeu = \"hr\", volumeu = \"L\") #> Loading required namespace: testthat #> $amtu #> [1] \"mg\" #> #> $clearanceu #> [1] \"L/h\" #> #> $volumeu #> [1] \"L\" #> #> $timeu #> [1] \"hr\" #> #> $dvu #> [1] \"ng/mL\" #> #> $cmtu #> [1] \"mg/L\" #> #> $dvConversion #> [1] 1000 #>"},{"path":"/reference/monolixControl.html","id":null,"dir":"Reference","previous_headings":"","what":"Monolix Controller for nlmixr2 — monolixControl","title":"Monolix Controller for nlmixr2 — monolixControl","text":"Monolix Controller nlmixr2","code":""},{"path":"/reference/monolixControl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Monolix Controller for nlmixr2 — monolixControl","text":"","code":"monolixControl( nbSSDoses = 7, useLinearization = FALSE, stiff = FALSE, addProp = c(\"combined2\", \"combined1\"), exploratoryAutoStop = FALSE, smoothingAutoStop = FALSE, burnInIterations = 5, smoothingIterations = 200, exploratoryIterations = 250, simulatedAnnealingIterations = 250, exploratoryInterval = 200, exploratoryAlpha = 0, omegaTau = 0.95, errorModelTau = 0.95, variability = c(\"none\", \"firstStage\", \"decreasing\"), runCommand = getOption(\"babelmixr2.monolix\", \"\"), rxControl = NULL, sumProd = FALSE, optExpression = TRUE, calcTables = TRUE, compress = TRUE, ci = 0.95, sigdigTable = NULL, absolutePath = FALSE, modelName = NULL, muRefCovAlg = TRUE, ... )"},{"path":"/reference/monolixControl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Monolix Controller for nlmixr2 — monolixControl","text":"nbSSDoses Number steady state doses (default 7) useLinearization Use linearization log likelihood fim. stiff boolean using stiff ODE solver addProp specifies type additive plus proportional errors, one standard deviations add (combined1) type variances add (combined2). combined1 error type can described following equation: y = f + (+ b*f^c)*err combined2 error model can described following equation: y = f + sqrt(^2 + b^2*(f^c)^2)*err : - y represents observed value - f represents predicted value - additive standard deviation - b proportional/power standard deviation - c power exponent (proportional case c=1) exploratoryAutoStop logical turn exploratory phase auto-stop SAEM (default 250) smoothingAutoStop Boolean indicating smoothing automatically stop (default `FALSE`) burnInIterations Number burn iterations smoothingIterations Number smoothing iterations exploratoryIterations Number iterations exploratory phase (default 250) simulatedAnnealingIterations Number simulating annealing iterations exploratoryInterval Minimum number iterations exploratory phase (default 200) exploratoryAlpha Convergence memory exploratory phase (used `exploratoryAutoStop` `TRUE`) omegaTau Proportional rate variance simulated annealing errorModelTau Proportional rate error model simulated annealing variability describes methodology parameters without variability. : - Fixed throughout (none) - Variability first stage (firstStage) - Decreasing reaches fixed value (decreasing) runCommand shell command function run monolix; can specify default options(\"babelmixr2.monolix\"=\"runMonolix\"). empty 'lixoftConnectors' available, use lixoftConnectors run monolix. See details function usage. rxControl `rxode2` ODE solving options fitting, created `rxControl()` sumProd boolean indicating model change multiplication high precision multiplication sums high precision sums using PreciseSums package. default FALSE. optExpression Optimize rxode2 expression speed calculation. default turned . calcTables boolean determine foceiFit calculate tables. default TRUE compress object compressed items ci Confidence level tables. default 0.95 95% confidence. sigdigTable Significant digits final output table. specified, matches significant digits `sigdig` optimization algorithm. `sigdig` NULL, use 3. absolutePath Boolean indicating absolute path used monolix runs modelName Model name used generate NONMEM output. `NULL` try infer model name (`x` clear). Otherwise use character outputs. muRefCovAlg controls algebraic expressions can mu-referenced treated mu-referenced covariates : 1. Creating internal data-variable `nlmixrMuDerCov#` algebraic mu-referenced expression 2. Change algebraic expression `nlmixrMuDerCov# * mu_cov_theta` 3. Use internal mu-referenced covariate saem 4. optimization completed, replace `model()` old `model()` expression 5. Remove `nlmixrMuDerCov#` nlmix2 output general, covariates accurate since changes system linear compartment model. Therefore, default `TRUE`. ... Ignored parameters","code":""},{"path":"/reference/monolixControl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Monolix Controller for nlmixr2 — monolixControl","text":"monolix control object","code":""},{"path":"/reference/monolixControl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Monolix Controller for nlmixr2 — monolixControl","text":"runCommand given string, called system() command like: runCommand mlxtran. example, runCommand=\"'/path//monolix/mlxbsub2021' -p \" command line used look like following: '/path//monolix/mlxbsub2021' monolix.mlxtran runCommand given function, called FUN(mlxtran, directory, ui) run Monolix. allows run Monolix way may need, long can write R. babelmixr2 wait function return proceeding. runCommand NA, nlmixr() stop writing model files without starting Monolix.","code":""},{"path":"/reference/monolixControl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Monolix Controller for nlmixr2 — monolixControl","text":"Matthew Fidler","code":""},{"path":"/reference/nlmixr2Est.pknca.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate starting parameters using PKNCA — nlmixr2Est.pknca","title":"Estimate starting parameters using PKNCA — nlmixr2Est.pknca","text":"Estimate starting parameters using PKNCA","code":""},{"path":"/reference/nlmixr2Est.pknca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate starting parameters using PKNCA — nlmixr2Est.pknca","text":"","code":"# S3 method for pknca nlmixr2Est(env, ...)"},{"path":"/reference/nlmixr2Est.pknca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate starting parameters using PKNCA — nlmixr2Est.pknca","text":"env Environment nlmixr2 estimation routines. needs : - rxode2 ui object `$ui` - data fit estimation routine `$data` - control estimation routine's control options `$ui` ... arguments provided `nlmixr2Est()` provided flexibility currently used inside nlmixr","code":""},{"path":"/reference/nlmixr2Est.pknca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate starting parameters using PKNCA — nlmixr2Est.pknca","text":"model updated starting parameters. model new element named \"nca\" available includes PKNCA results used calculation.","code":""},{"path":"/reference/nlmixr2Est.pknca.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate starting parameters using PKNCA — nlmixr2Est.pknca","text":"Parameters estimated follows: ka4 half-lives Tmax higher 3: log(2)/(tmax/4) vcInverse dose-normalized Cmax clEstimated median clearance vp,vp22- 4-fold vc, respectively default, controlled vpMult vp2Mult arguments pkncaControl q,q20.5- 0.25-fold cl, respectively default, controlled qMult q2Mult arguments pkncaControl bounds parameter estimates set 10 10 times 99th percentile. (ka, lower bound set lower 10 modified 10 times 99th percentile.) Parameter estimation methods may changed future version.","code":""},{"path":"/reference/nmGetDistributionMonolixLines.html","id":null,"dir":"Reference","previous_headings":"","what":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionMonolixLines","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionMonolixLines","text":"S3 method getting distribution lines base rxode2 saem problem","code":""},{"path":"/reference/nmGetDistributionMonolixLines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionMonolixLines","text":"","code":"nmGetDistributionMonolixLines(line) # S3 method for rxUi nmGetDistributionMonolixLines(line) # S3 method for norm nmGetDistributionMonolixLines(line)"},{"path":"/reference/nmGetDistributionMonolixLines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionMonolixLines","text":"line Parsed rxode2 model environment","code":""},{"path":"/reference/nmGetDistributionMonolixLines.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionMonolixLines","text":"Lines estimation monolix","code":""},{"path":"/reference/nmGetDistributionMonolixLines.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionMonolixLines","text":"Matthew Fidler","code":""},{"path":"/reference/nmGetDistributionNonmemLines.html","id":null,"dir":"Reference","previous_headings":"","what":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionNonmemLines","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionNonmemLines","text":"S3 method getting distribution lines base rxode2 saem problem","code":""},{"path":"/reference/nmGetDistributionNonmemLines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionNonmemLines","text":"","code":"nmGetDistributionNonmemLines(line) # S3 method for rxUi nmGetDistributionNonmemLines(line) # S3 method for norm nmGetDistributionNonmemLines(line)"},{"path":"/reference/nmGetDistributionNonmemLines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionNonmemLines","text":"line Parsed rxode2 model environment","code":""},{"path":"/reference/nmGetDistributionNonmemLines.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionNonmemLines","text":"Lines estimation nonmem","code":""},{"path":"/reference/nmGetDistributionNonmemLines.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionNonmemLines","text":"Matthew Fidler","code":""},{"path":"/reference/nonmemControl.html","id":null,"dir":"Reference","previous_headings":"","what":"NONMEM estimation control — nonmemControl","title":"NONMEM estimation control — nonmemControl","text":"NONMEM estimation control","code":""},{"path":"/reference/nonmemControl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"NONMEM estimation control — nonmemControl","text":"","code":"nonmemControl( est = c(\"focei\", \"imp\", \"its\", \"posthoc\"), advanOde = c(\"advan13\", \"advan8\", \"advan6\"), cov = c(\"r,s\", \"r\", \"s\", \"\"), maxeval = 1e+05, tol = 6, atol = 12, sstol = 6, ssatol = 12, sigl = 12, sigdig = 3, print = 1, extension = getOption(\"babelmixr2.nmModelExtension\", \".nmctl\"), outputExtension = getOption(\"babelmixr2.nmOutputExtension\", \".lst\"), runCommand = getOption(\"babelmixr2.nonmem\", \"\"), iniSigDig = 5, protectZeros = TRUE, muRef = TRUE, addProp = c(\"combined2\", \"combined1\"), rxControl = NULL, sumProd = FALSE, optExpression = TRUE, calcTables = TRUE, compress = TRUE, ci = 0.95, sigdigTable = NULL, readRounding = FALSE, readBadOpt = FALSE, niter = 100L, isample = 1000L, iaccept = 0.4, iscaleMin = 0.1, iscaleMax = 10, df = 4, seed = 14456, mapiter = 1, mapinter = 0, noabort = TRUE, modelName = NULL, muRefCovAlg = TRUE, ... )"},{"path":"/reference/nonmemControl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"NONMEM estimation control — nonmemControl","text":"est NONMEM estimation method advanOde ODE solving method NONMEM cov NONMEM covariance method maxeval NONMEM's maxeval (non posthoc methods) tol NONMEM tolerance ODE solving advan atol NONMEM absolute tolerance ODE solving sstol NONMEM tolerance steady state ODE solving ssatol NONMEM absolute tolerance steady state ODE solving sigl NONMEM sigl estimation option sigdig significant digits NONMEM print print number NONMEM extension NONMEM file extensions outputExtension Extension use NONMEM output listing runCommand Command run NONMEM (typically path \"nmfe75\") function. See details information. iniSigDig many significant digits printed $THETA $OMEGA estimate zero. Also controls zero protection numbers protectZeros Add methods protect divide zero muRef Automatically mu-reference control stream addProp, sumProd, optExpression, calcTables, compress, ci, sigdigTable Passed nlmixr2est::foceiControl rxControl Options pass rxode2::rxControl simulations readRounding Try read NONMEM output NONMEM terminated due rounding errors readBadOpt Try read NONMEM output NONMEM terminated due apparent failed optimization niter number iterations NONMEM estimation methods isample Isample argument NONMEM estimation method iaccept Iaccept NONMEM estimation methods iscaleMin parameter IMP NONMEM method (ISCALE_MIN) iscaleMax parameter IMP NONMEM method (ISCALE_MAX) df degrees freedom IMP method seed seed NONMEM methods mapiter number map iterations IMP method mapinter MAPINTER parameter IMP method noabort Add `NOABORT` option `$EST` modelName Model name used generate NONMEM output. `NULL` try infer model name (`x` clear). Otherwise use character outputs. muRefCovAlg controls algebraic expressions can mu-referenced treated mu-referenced covariates : 1. Creating internal data-variable `nlmixrMuDerCov#` algebraic mu-referenced expression 2. Change algebraic expression `nlmixrMuDerCov# * mu_cov_theta` 3. Use internal mu-referenced covariate saem 4. optimization completed, replace `model()` old `model()` expression 5. Remove `nlmixrMuDerCov#` nlmix2 output general, covariates accurate since changes system linear compartment model. Therefore, default `TRUE`. ... optional genRxControl argument controlling automatic rxControl generation.","code":""},{"path":"/reference/nonmemControl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"NONMEM estimation control — nonmemControl","text":"babelmixr2 control option generating NONMEM control stream reading back `babelmixr2`/`nlmixr2`","code":""},{"path":"/reference/nonmemControl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"NONMEM estimation control — nonmemControl","text":"runCommand given string, called system() command like: runCommand controlFile outputFile. example, runCommand=\"'/path//nmfe75'\" command line used look like following: '/path//nmfe75' one.cmt.nmctl one.cmt.lst runCommand given function, called FUN(ctl, directory, ui) run NONMEM. allows run NONMEM way may need, long can write R. babelmixr2 wait function return proceeding. runCommand NA, nlmixr() stop writing model files without starting NONMEM.","code":""},{"path":"/reference/nonmemControl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"NONMEM estimation control — nonmemControl","text":"Matthew L. Fidler","code":""},{"path":"/reference/nonmemControl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"NONMEM estimation control — nonmemControl","text":"","code":"nonmemControl() #> $est #> [1] \"focei\" #> #> $cov #> [1] \"r,s\" #> #> $advanOde #> [1] \"advan13\" #> #> $maxeval #> [1] 1e+05 #> #> $print #> [1] 1 #> #> $noabort #> [1] TRUE #> #> $iniSigDig #> [1] 5 #> #> $tol #> [1] 6 #> #> $atol #> [1] 12 #> #> $sstol #> [1] 6 #> #> $ssatol #> [1] 12 #> #> $sigl #> [1] 12 #> #> $muRef #> [1] TRUE #> #> $sigdig #> [1] 3 #> #> $protectZeros #> [1] TRUE #> #> $runCommand #> [1] \"\" #> #> $outputExtension #> [1] \".lst\" #> #> $addProp #> [1] \"combined2\" #> #> $rxControl #> $scale #> NULL #> #> $method #> liblsoda #> 2 #> #> $atol #> [1] 1e-12 #> #> $rtol #> [1] 1e-06 #> #> $maxsteps #> [1] 70000 #> #> $hmin #> [1] 0 #> #> $hmax #> [1] NA #> #> $hini #> [1] 0 #> #> $maxordn #> [1] 12 #> #> $maxords #> [1] 5 #> #> $covsInterpolation #> nocb #> 2 #> #> $addCov #> [1] TRUE #> #> $returnType #> rxSolve #> 0 #> #> $sigma #> NULL #> #> $sigmaDf #> NULL #> #> $nCoresRV #> [1] 1 #> #> $sigmaIsChol #> [1] FALSE #> #> $sigmaSeparation #> [1] \"auto\" #> #> $sigmaXform #> identity #> 4 #> #> $nDisplayProgress #> [1] 10000 #> #> $amountUnits #> [1] NA #> #> $timeUnits #> [1] \"hours\" #> #> $addDosing #> [1] FALSE #> #> $stateTrim #> [1] Inf #> #> $updateObject #> [1] FALSE #> #> $omega #> NULL #> #> $omegaDf #> NULL #> #> $omegaIsChol #> [1] FALSE #> #> $omegaSeparation #> [1] \"auto\" #> #> $omegaXform #> variance #> 6 #> #> $nSub #> [1] 1 #> #> $thetaMat #> NULL #> #> $thetaDf #> NULL #> #> $thetaIsChol #> [1] FALSE #> #> $nStud #> [1] 1 #> #> $dfSub #> [1] 0 #> #> $dfObs #> [1] 0 #> #> $seed #> NULL #> #> $nsim #> NULL #> #> $minSS #> [1] 10 #> #> $maxSS #> [1] 1000 #> #> $strictSS #> [1] 1 #> #> $infSSstep #> [1] 12 #> #> $istateReset #> [1] TRUE #> #> $subsetNonmem #> [1] TRUE #> #> $hmaxSd #> [1] 0 #> #> $maxAtolRtolFactor #> [1] 0.1 #> #> $from #> NULL #> #> $to #> NULL #> #> $by #> NULL #> #> $length.out #> NULL #> #> $iCov #> NULL #> #> $keep #> NULL #> #> $keepF #> character(0) #> #> $drop #> NULL #> #> $warnDrop #> [1] TRUE #> #> $omegaLower #> [1] -Inf #> #> $omegaUpper #> [1] Inf #> #> $sigmaLower #> [1] -Inf #> #> $sigmaUpper #> [1] Inf #> #> $thetaLower #> [1] -Inf #> #> $thetaUpper #> [1] Inf #> #> $indLinPhiM #> [1] 0 #> #> $indLinPhiTol #> [1] 1e-07 #> #> $indLinMatExpType #> expokit #> 2 #> #> $indLinMatExpOrder #> [1] 6 #> #> $idFactor #> [1] TRUE #> #> $mxhnil #> [1] 0 #> #> $hmxi #> [1] 0 #> #> $warnIdSort #> [1] TRUE #> #> $ssAtol #> [1] 1e-12 #> #> $ssRtol #> [1] 1e-06 #> #> $safeZero #> [1] 0 #> #> $sumType #> pairwise #> 1 #> #> $prodType #> long double #> 1 #> #> $sensType #> advan #> 4 #> #> $linDiff #> tlag f rate dur tlag2 f2 rate2 dur2 #> 1.5e-05 1.5e-05 1.5e-05 1.5e-05 1.5e-05 1.5e-05 1.5e-05 1.5e-05 #> #> $linDiffCentral #> tlag f rate dur tlag2 f2 rate2 dur2 #> TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE #> #> $resample #> NULL #> #> $resampleID #> [1] TRUE #> #> $maxwhile #> [1] 100000 #> #> $cores #> [1] 0 #> #> $atolSens #> [1] 1e-08 #> #> $rtolSens #> [1] 1e-06 #> #> $ssAtolSens #> [1] 1e-08 #> #> $ssRtolSens #> [1] 1e-06 #> #> $simVariability #> [1] NA #> #> $nLlikAlloc #> NULL #> #> $useStdPow #> [1] 0 #> #> $naTimeHandle #> ignore #> 1 #> #> $addlKeepsCov #> [1] FALSE #> #> $addlDropSs #> [1] TRUE #> #> $ssAtDoseTime #> [1] TRUE #> #> $ss2cancelAllPending #> [1] FALSE #> #> $.zeros #> NULL #> #> attr(,\"class\") #> [1] \"rxControl\" #> #> $sumProd #> [1] FALSE #> #> $optExpression #> [1] TRUE #> #> $calcTables #> [1] TRUE #> #> $compress #> [1] TRUE #> #> $ci #> [1] 0.95 #> #> $sigdigTable #> NULL #> #> $readRounding #> [1] FALSE #> #> $readBadOpt #> [1] FALSE #> #> $genRxControl #> [1] TRUE #> #> $niter #> [1] 100 #> #> $isample #> [1] 1000 #> #> $iaccept #> [1] 0.4 #> #> $iscaleMin #> [1] 0.1 #> #> $iscaleMax #> [1] 10 #> #> $df #> [1] 4 #> #> $seed #> [1] 14456 #> #> $mapiter #> [1] 1 #> #> $modelName #> NULL #> #> $muRefCovAlg #> [1] TRUE #> #> attr(,\"class\") #> [1] \"nonmemControl\""},{"path":"/reference/pkncaControl.html","id":null,"dir":"Reference","previous_headings":"","what":"PKNCA estimation control — pkncaControl","title":"PKNCA estimation control — pkncaControl","text":"PKNCA estimation control","code":""},{"path":"/reference/pkncaControl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PKNCA estimation control — pkncaControl","text":"","code":"pkncaControl( concu = NA_character_, doseu = NA_character_, timeu = NA_character_, volumeu = NA_character_, vpMult = 2, qMult = 1/2, vp2Mult = 4, q2Mult = 1/4, dvParam = \"cp\", groups = character(), sparse = FALSE, ncaData = NULL, ncaResults = NULL, rxControl = rxode2::rxControl() )"},{"path":"/reference/pkncaControl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PKNCA estimation control — pkncaControl","text":"concu, doseu, timeu concentration, dose, time units source data (passed PKNCA::pknca_units_table()). volumeu compartment volume model (NULL, simplified units source data used) vpMult, qMult, vp2Mult, q2Mult Multipliers vc cl provide initial estimates vp, q, vp2, q2 dvParam parameter name model modified concentration unit conversions. must assigned line , separate residual error model line. groups Grouping columns NCA summaries group (required sparse = TRUE) sparse concentration-time data sparse PK (commonly used small nonclinical species terminal difficult sampling) dense PK (commonly used clinical studies larger nonclinical species)? ncaData Data use calculating NCA parameters. Typical use subset original data informative NCA. ncaResults Already computed NCA results (PKNCAresults object) bypass automatic calculations. least following parameters must calculated NCA: tmax, cmax.dn, cl.last","code":""},{"path":"/reference/pkncaControl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PKNCA estimation control — pkncaControl","text":"list parameters","code":""},{"path":"/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. nlmixr2est getValidNlmixrCtl, nlmixr2Est, nmObjGetControl, nmObjGetFoceiControl, nmObjHandleControlObject nonmem2rx .nonmem2rx, nonmem2rx rxode2 .minfo, rxModelVars, rxUiGet","code":""},{"path":"/reference/rxToMonolix.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert RxODE syntax to monolix syntax — rxToMonolix","title":"Convert RxODE syntax to monolix syntax — rxToMonolix","text":"Convert RxODE syntax monolix syntax","code":""},{"path":"/reference/rxToMonolix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert RxODE syntax to monolix syntax — rxToMonolix","text":"","code":"rxToMonolix(x, ui)"},{"path":"/reference/rxToMonolix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert RxODE syntax to monolix syntax — rxToMonolix","text":"x Expression ui rxode2 ui","code":""},{"path":"/reference/rxToMonolix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert RxODE syntax to monolix syntax — rxToMonolix","text":"Monolix syntax","code":""},{"path":"/reference/rxToMonolix.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convert RxODE syntax to monolix syntax — rxToMonolix","text":"Matthew Fidler","code":""},{"path":"/reference/rxToNonmem.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert RxODE syntax to NONMEM syntax — rxToNonmem","title":"Convert RxODE syntax to NONMEM syntax — rxToNonmem","text":"Convert RxODE syntax NONMEM syntax","code":""},{"path":"/reference/rxToNonmem.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert RxODE syntax to NONMEM syntax — rxToNonmem","text":"","code":"rxToNonmem(x, ui)"},{"path":"/reference/rxToNonmem.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert RxODE syntax to NONMEM syntax — rxToNonmem","text":"x Expression ui rxode2 ui","code":""},{"path":"/reference/rxToNonmem.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert RxODE syntax to NONMEM syntax — rxToNonmem","text":"NONMEM syntax","code":""},{"path":"/reference/rxToNonmem.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convert RxODE syntax to NONMEM syntax — rxToNonmem","text":"Matthew Fidler","code":""},{"path":"/reference/simplifyUnit.html","id":null,"dir":"Reference","previous_headings":"","what":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","title":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","text":"Simplify units removing repeated units numerator denominator","code":""},{"path":"/reference/simplifyUnit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","text":"","code":"simplifyUnit(numerator = \"\", denominator = \"\")"},{"path":"/reference/simplifyUnit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","text":"numerator numerator units (whole unit specification) denominator denominator units (NULL numerator whole unit specification)","code":""},{"path":"/reference/simplifyUnit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","text":"units specified units numerator denominator cancelled.","code":""},{"path":"/reference/simplifyUnit.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","text":"NA \"\" numerator denominator considered unitless.","code":""},{"path":[]},{"path":"/reference/simplifyUnit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","text":"","code":"simplifyUnit(\"kg\", \"kg/mL\") #> [1] \"mL\" # units that don't match exactly are not cancelled simplifyUnit(\"kg\", \"g/mL\") #> [1] \"kg*mL/g\""},{"path":"/news/index.html","id":"babelmixr2-development-version","dir":"Changelog","previous_headings":"","what":"babelmixr2 (development version)","title":"babelmixr2 (development version)","text":"Handle algebraic mu expressions PKNCA controller now contains rxControl since used translation options revision load pruned ui model query compartment properties (.e. bioavailability, lag time, etc) writing NONMEM model. fix issues PK block define variables larger calculated variable can used model instead. nonmem2rx different lst file, long nonmem2rx::nminfo(file) works, successful conversion nlmixr2 fit object occur. Fix save parameter history $parHistData accommodate changes focei’s output ($parHist now derived). Changed solving options match new steady state options rxode2 NONMEM implements . Also changed itwres model account rxerr. instead err. updated rxode2 well.","code":""},{"path":"/news/index.html","id":"babelmixr2-011","dir":"Changelog","previous_headings":"","what":"babelmixr2 0.1.1","title":"babelmixr2 0.1.1","text":"CRAN release: 2023-05-27 Add new method .nlmixr2 convert nonmem2rx methods nlmixr fits Dropped pmxTools favor nonmem2rx conserve methods","code":""},{"path":"/news/index.html","id":"babelmixr2-010","dir":"Changelog","previous_headings":"","what":"babelmixr2 0.1.0","title":"babelmixr2 0.1.0","text":"CRAN release: 2022-10-28 Babelmixr support “monolix”, “nonmem”, “pknca” methods release. Added NEWS.md file track changes package.","code":""}] +[{"path":"/articles/new-estimation.html","id":"create-a-nlmixr2est-method","dir":"Articles","previous_headings":"","what":"Create a nlmixr2Est() method","title":"Creating a New Estimation Method","text":"method input environment nlmixr2est UI object (see ?nlmixr2Est). output fit object.","code":""},{"path":"/articles/new-estimation.html","id":"create-a-control-method","dir":"Articles","previous_headings":"","what":"Create a control method","title":"Creating a New Estimation Method","text":"control method gives access controls required estimation.","code":""},{"path":"/articles/running-monlix.html","id":"step-0-what-do-you-need-to-do-to-have-nlmixr2-run-monolix-from-a-nlmixr2-model","dir":"Articles","previous_headings":"","what":"Step 0: What do you need to do to have nlmixr2 run Monolix from a nlmixr2 model","title":"Running Monolix","text":"use Monolix nlmixr2, need change data nlmixr2 dataset. babelmixr2 heavy lifting . need setup run Monolix. setup lixoftConnectors package Monolix, setup needed. Instead run Monolix command line grid processing (example) can figure command run Monolix (often useful use full command path set options, ie options(\"babelmixr2.monolix\"=\"monolix\") use monolixControl(runCommand=\"monolix\"). needed, prefer options() method since need set . also function prefer (cover using function ).","code":""},{"path":"/articles/running-monlix.html","id":"step-1-run-a-nlmixr2-in-monolix","dir":"Articles","previous_headings":"","what":"Step 1: Run a nlmixr2 in Monolix","title":"Running Monolix","text":"Lets take classic warfarin example. model use nlmixr2 vignettes : monolix run, can run nlmixr2 model using Monolix new estimation method: fit issues informational tidbit - monolix parameter history needs exported charts, please export charts automatically generated well lixoftConnectors package generated recent version Monolix. don’t information important parameter history plots imported see plots. Just like NONMEM translation, monolixControl() modelName helps control output directory Monolix (specified babelmixr2 tries guess based model name based input). Printing nlmixr2 fit see: particular interest comparison Monolix predictions nlmixr predictions. case, believe also imply models predicting thing. Note model predictions close NONMEM Monolix use lsoda ODE solver. Hence small deviation expected, still gives validated Monolix model.","code":"pk.turnover.emax3 <- function() { ini({ tktr <- log(1) tka <- log(1) tcl <- log(0.1) tv <- log(10) ## eta.ktr ~ 1 eta.ka ~ 1 eta.cl ~ 2 eta.v ~ 1 prop.err <- 0.1 pkadd.err <- 0.1 ## temax <- logit(0.8) tec50 <- log(0.5) tkout <- log(0.05) te0 <- log(100) ## eta.emax ~ .5 eta.ec50 ~ .5 eta.kout ~ .5 eta.e0 ~ .5 ## pdadd.err <- 10 }) model({ ktr <- exp(tktr + eta.ktr) ka <- exp(tka + eta.ka) cl <- exp(tcl + eta.cl) v <- exp(tv + eta.v) emax = expit(temax+eta.emax) ec50 = exp(tec50 + eta.ec50) kout = exp(tkout + eta.kout) e0 = exp(te0 + eta.e0) ## DCP = center/v PD=1-emax*DCP/(ec50+DCP) ## effect(0) = e0 kin = e0*kout ## d/dt(depot) = -ktr * depot d/dt(gut) = ktr * depot -ka * gut d/dt(center) = ka * gut - cl / v * center d/dt(effect) = kin*PD -kout*effect ## cp = center / v cp ~ prop(prop.err) + add(pkadd.err) effect ~ add(pdadd.err) | pca }) } fit <- nlmixr(pk.turnover.emax3, nlmixr2data::warfarin, \"monolix\", monolixControl(modelName=\"pk.turnover.emax3\")) #> ℹ assuming monolix is running because 'pk.turnover.emax3-monolix.txt' is present #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → finding duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling EBE model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → Calculating residuals/tables #> ✔ done #> → compress origData in nlmixr2 object, save 27560 #> ℹ monolix parameter history needs exported charts, please export charts fit #> ── nlmixr² monolix ver 2021R1 ── #> #> OBJF AIC BIC Log-likelihood Condition#(Cov) #> monolix 1522.704 2448.398 2527.819 -1205.199 2203.836 #> Condition#(Cor) #> monolix 2.697324 #> #> ── Time (sec fit$time): ── #> #> setup table compress other #> elapsed 0.00353 0.093 0.009 5.08647 #> #> ── Population Parameters (fit$parFixed or fit$parFixedDf): ── #> #> Est. SE %RSE Back-transformed(95%CI) BSV(CV% or SD) #> tktr 0.218 0.179 82 1.24 (0.876, 1.77) 84.0 #> tka 0.00533 0.117 2.19e+03 1.01 (0.8, 1.26) 48.6 #> tcl -2.01 0.0518 2.58 0.135 (0.122, 0.149) 28.5 #> tv 2.04 0.0438 2.14 7.73 (7.09, 8.42) 22.6 #> prop.err 0.0986 0.0986 #> pkadd.err 0.533 0.533 #> temax 4.46 0.527 11.8 0.989 (0.969, 0.996) 0.380 #> tec50 0.0786 0.0889 113 1.08 (0.909, 1.29) 47.8 #> tkout -2.94 0.0261 0.888 0.053 (0.0503, 0.0558) 7.87 #> te0 4.57 0.0114 0.249 96.7 (94.5, 98.9) 5.08 #> pdadd.err 3.79 3.79 #> Shrink(SD)% #> tktr 47.9% #> tka 48.9% #> tcl 1.25% #> tv 6.09% #> prop.err #> pkadd.err #> temax 91.9% #> tec50 6.29% #> tkout 36.6% #> te0 19.9% #> pdadd.err #> #> Covariance Type (fit$covMethod): MonolixLin #> No correlations in between subject variability (BSV) matrix #> Full BSV covariance (fit$omega) or correlation (fit$omegaR; diagonals=SDs) #> Distribution stats (mean/skewness/kurtosis/p-value) available in fit$shrink #> Censoring (fit$censInformation): No censoring #> Minimization message (fit$message): #> IPRED relative difference compared to Monolix IPRED: 0.09%; 95% percentile: (0.01%,0.49%); rtol=0.000941 #> PRED relative difference compared to Monolix PRED: 0.04%; 95% percentile: (0%,0.2%); rtol=0.000428 #> IPRED absolute difference compared to Monolix IPRED: atol=0.00911; 95% percentile: (0.000493, 0.0928) #> PRED absolute difference compared to Monolix PRED: atol=0.000428; 95% percentile: (3.14e-07, 0.203) #> monolix model: 'pk.turnover.emax3-monolix.mlxtran' #> #> ── Fit Data (object fit is a modified tibble): ── #> # A tibble: 483 × 35 #> ID TIME CMT DV PRED RES IPRED IRES IWRES eta.ktr eta.ka eta.cl #> #> 1 1 0.5 cp 0 1.40 -1.40 0.500 -0.500 -0.934 -0.638 -0.447 0.689 #> 2 1 1 cp 1.9 3.94 -2.04 1.62 0.284 0.511 -0.638 -0.447 0.689 #> 3 1 2 cp 3.3 8.30 -5.00 4.29 -0.987 -1.45 -0.638 -0.447 0.689 #> # ℹ 480 more rows #> # ℹ 23 more variables: eta.v , eta.emax , eta.ec50 , #> # eta.kout , eta.e0 , cp , depot , gut , #> # center , effect , ktr , ka , cl , v , #> # emax , ec50 , kout , e0 , DCP , PD , #> # kin , tad , dosenum "},{"path":"/articles/running-monlix.html","id":"optional-step-2-add-conditional-weighted-residualsfocei-objf-to-monolix","dir":"Articles","previous_headings":"","what":"Optional Step 2: Add conditional weighted residuals/focei objf to Monolix","title":"Running Monolix","text":"case NONMEM, gives things available Monolix, like adding conditional weighted residuals: add nlmixr’s CWRES well adding nlmixr2 FOCEi objective function now objective function compared based assumptions, compare performance Monolix NONMEM based objective function. fair, objective function values must always used caution. model performs predicts data far valuable.","code":"fit <- addCwres(fit) #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → calculate jacobian #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate sensitivities #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate ∂(f)/∂(η) #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate ∂(R²)/∂(η) #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → finding duplicate expressions in inner model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in inner model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → finding duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling inner model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → finding duplicate expressions in FD model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in FD model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling EBE model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → compiling events FD model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → Calculating residuals/tables #> ✔ done"},{"path":"/articles/running-monlix.html","id":"optional-step-3-use-nlmixr2-for-vpc-reporting-etc-","dir":"Articles","previous_headings":"","what":"Optional Step 3: Use nlmixr2 for vpc, reporting, etc.","title":"Running Monolix","text":"Also since nlmixr2 object easy perform VPC :","code":"v1s <- vpcPlot(fit, show=list(obs_dv=TRUE), scales=\"free_y\") + ylab(\"Warfarin Cp [mg/L] or PCA\") + xlab(\"Time [h]\") #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ v2s <- vpcPlot(fit, show=list(obs_dv=TRUE), pred_corr = TRUE, scales=\"free_y\") + ylab(\"Prediction Corrected Warfarin Cp [mg/L] or PCA\") + xlab(\"Time [h]\") v1s v2s"},{"path":"/articles/running-monlix.html","id":"notes-about-monolix-data-translation","dir":"Articles","previous_headings":"","what":"Notes about Monolix data translation","title":"Running Monolix","text":"input dataset expected compatible rxode2 nlmixr2. dataset converted Monolix format: combination CMT Dose type creates unique ADM variable. ADM definition saved monolix model file babelmixr2 creates macro describing compartment, ie compartment(cmt=#, amount=stateName) babelmixr2 also creates macro type dosing: Bolus/infusion uses depot() adds modeled lag time (Tlag) bioavailability (p) specified Modeled rate uses depot() Tk0=amtDose/rate. babelmixr2 also adds modeled lag time (Tlag) bioavailability (p) specified Modeled duration uses depot() Tk0=dur, also add adds modeled lag time (Tlag) bioavailability (p) specified Turning compartment uses empty macro","code":""},{"path":"/articles/running-nonmem.html","id":"step-0-what-do-you-need-to-do-to-have-nlmixr2-run-nonmem-from-a-nlmixr2-model","dir":"Articles","previous_headings":"","what":"Step 0: What do you need to do to have nlmixr2 run NONMEM from a nlmixr2 model","title":"Running NONMEM with nlmixr2","text":"use NONMEM nlmixr, need change data nlmixr2 dataset. babelmixr2 heavy lifting . need setup run NONMEM. many cases easy; simply figure command run NONMEM (often useful use full command path). can set options(\"babelmixr2.nonmem\"=\"nmfe743\") use nonmemControl(runCommand=\"nmfe743\"). prefer options() method since need set . also function prefer (cover using function ).","code":""},{"path":"/articles/running-nonmem.html","id":"step-1-run-a-nlmixr2-in-nonmem","dir":"Articles","previous_headings":"","what":"Step 1: Run a nlmixr2 in NONMEM","title":"Running NONMEM with nlmixr2","text":"Lets take classic warfarin example start comparison. model use nlmixr2 vignettes : Now can run nlmixr2 model using NONMEM simply can run directly: way run ordinary nlmixr2 model, simply new estimation method \"nonmem\" new controller (nonmemControl()) setup options estimation. options nonmemControl() modelName helps control output directory NONMEM (specified babelmixr2 tries guess based model name based input). try , see NONMEM fails rounding errors. standard approach changing sigdig, sigl, tol etc, get successful NONMEM model convergence, course supported. babelmixr2 can .","code":"library(babelmixr2) pk.turnover.emax3 <- function() { ini({ tktr <- log(1) tka <- log(1) tcl <- log(0.1) tv <- log(10) ## eta.ktr ~ 1 eta.ka ~ 1 eta.cl ~ 2 eta.v ~ 1 prop.err <- 0.1 pkadd.err <- 0.1 ## temax <- logit(0.8) tec50 <- log(0.5) tkout <- log(0.05) te0 <- log(100) ## eta.emax ~ .5 eta.ec50 ~ .5 eta.kout ~ .5 eta.e0 ~ .5 ## pdadd.err <- 10 }) model({ ktr <- exp(tktr + eta.ktr) ka <- exp(tka + eta.ka) cl <- exp(tcl + eta.cl) v <- exp(tv + eta.v) emax = expit(temax+eta.emax) ec50 = exp(tec50 + eta.ec50) kout = exp(tkout + eta.kout) e0 = exp(te0 + eta.e0) ## DCP = center/v PD=1-emax*DCP/(ec50+DCP) ## effect(0) = e0 kin = e0*kout ## d/dt(depot) = -ktr * depot d/dt(gut) = ktr * depot -ka * gut d/dt(center) = ka * gut - cl / v * center d/dt(effect) = kin*PD -kout*effect ## cp = center / v cp ~ prop(prop.err) + add(pkadd.err) effect ~ add(pdadd.err) | pca }) } try(nlmixr(pk.turnover.emax3, nlmixr2data::warfarin, \"nonmem\", nonmemControl(readRounding=FALSE, modelName=\"pk.turnover.emax3\")), silent=TRUE) #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> #> #> WARNINGS AND ERRORS (IF ANY) FOR PROBLEM 1 #> #> (WARNING 2) NM-TRAN INFERS THAT THE DATA ARE POPULATION. #> #> #> 0MINIMIZATION TERMINATED #> DUE TO ROUNDING ERRORS (ERROR=134) #> NO. OF FUNCTION EVALUATIONS USED: 1088 #> NO. OF SIG. DIGITS UNREPORTABLE #> 0PARAMETER ESTIMATE IS NEAR ITS BOUNDARY #> #> nonmem model: 'pk.turnover.emax3-nonmem/pk.turnover.emax3.nmctl' #> → terminated with rounding errors, can force nlmixr2/rxode2 to read with nonmemControl(readRounding=TRUE) #> Error : nonmem minimization not successful"},{"path":"/articles/running-nonmem.html","id":"optional-step-2-recover-a-failed-nonmem-run","dir":"Articles","previous_headings":"","what":"Optional Step 2: Recover a failed NONMEM run","title":"Running NONMEM with nlmixr2","text":"One approaches ignore rounding errors occurred read nlmixr2 anyway: may see work happening expected need already completed model. reading NONMEM model, babelmixr2 grabs: NONMEM’s objective function value NONMEM’s covariance (available) NONMEM’s optimization history NONMEM’s final parameter estimates (including ETAs) NONMEM’s PRED IPRED values (validation purposes) used solve ODEs came nlmixr2 optimization procedure. means can compare IPRED PRED values nlmixr2/rxode2 know immediately model validates. similar procedure Kyle Baron advocates validating NONMEM model mrgsolve model (see https://mrgsolve.org/blog/posts/2022-05-validate-translation/ https://mrgsolve.org/blog/posts/2023-update-validation.html), advantage method need simply write one model get validated roxde2/nlmixr2 model. case can see validation print fit object: shows preds ipreds match NONMEM nlmixr2 quite well.","code":"# Can still load the model to get information (possibly pipe) and create a new model f <- nlmixr(pk.turnover.emax3, nlmixr2data::warfarin, \"nonmem\", nonmemControl(readRounding=TRUE, modelName=\"pk.turnover.emax3\")) #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → finding duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling EBE model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> rxode2 2.0.14.9000 using 1 threads (see ?getRxThreads) #> no cache: create with `rxCreateCache()` #> → Calculating residuals/tables #> ✔ done #> → compress origData in nlmixr2 object, save 27560 #> → compress parHistData in nlmixr2 object, save 5536 print(f) #> ── nlmixr² nonmem ver 7.4.3 ── #> #> OBJF AIC BIC Log-likelihood Condition#(Cov) #> nonmem focei 1326.91 2252.605 2332.025 -1107.302 NA #> Condition#(Cor) #> nonmem focei NA #> #> ── Time (sec $time): ── #> #> setup table compress NONMEM #> elapsed 0.027612 0.058 0.015 320.27 #> #> ── Population Parameters ($parFixed or $parFixedDf): ── #> #> Est. Back-transformed BSV(CV% or SD) Shrink(SD)% #> tktr 6.24e-07 1 86.5 59.8% #> tka -3.01e-06 1 86.5 59.8% #> tcl -2 0.135 28.6 1.34% #> tv 2.05 7.78 22.8 6.44% #> prop.err 0.0986 0.0986 #> pkadd.err 0.512 0.512 #> temax 6.42 0.998 0.00707 100.% #> tec50 0.141 1.15 45.0 6.06% #> tkout -2.95 0.0522 9.16 32.4% #> te0 4.57 96.6 5.24 18.1% #> pdadd.err 3.72 3.72 #> #> No correlations in between subject variability (BSV) matrix #> Full BSV covariance ($omega) or correlation ($omegaR; diagonals=SDs) #> Distribution stats (mean/skewness/kurtosis/p-value) available in $shrink #> Information about run found ($runInfo): #> • NONMEM terminated due to rounding errors, but reading into nlmixr2/rxode2 anyway #> Censoring ($censInformation): No censoring #> Minimization message ($message): #> #> #> WARNINGS AND ERRORS (IF ANY) FOR PROBLEM 1 #> #> (WARNING 2) NM-TRAN INFERS THAT THE DATA ARE POPULATION. #> #> #> 0MINIMIZATION TERMINATED #> DUE TO ROUNDING ERRORS (ERROR=134) #> NO. OF FUNCTION EVALUATIONS USED: 1088 #> NO. OF SIG. DIGITS UNREPORTABLE #> 0PARAMETER ESTIMATE IS NEAR ITS BOUNDARY #> #> IPRED relative difference compared to Nonmem IPRED: 0%; 95% percentile: (0%,0%); rtol=6.36e-06 #> PRED relative difference compared to Nonmem PRED: 0%; 95% percentile: (0%,0%); rtol=6.08e-06 #> IPRED absolute difference compared to Nonmem IPRED: 95% percentile: (2.53e-06, 0.000502); atol=7.15e-05 #> PRED absolute difference compared to Nonmem PRED: 95% percentile: (3.79e-07,0.00321); atol=6.08e-06 #> there are solving errors during optimization (see '$prderr') #> nonmem model: 'pk.turnover.emax3-nonmem/pk.turnover.emax3.nmctl' #> #> ── Fit Data (object is a modified tibble): ── #> # A tibble: 483 × 35 #> ID TIME CMT DV PRED RES IPRED IRES IWRES eta.ktr eta.ka eta.cl #> #> 1 1 0.5 cp 0 1.16 -1.16 0.444 -0.444 -0.864 -0.506 -0.506 0.699 #> 2 1 1 cp 1.9 3.37 -1.47 1.45 0.446 0.840 -0.506 -0.506 0.699 #> 3 1 2 cp 3.3 7.51 -4.21 3.96 -0.660 -1.03 -0.506 -0.506 0.699 #> # ℹ 480 more rows #> # ℹ 23 more variables: eta.v , eta.emax , eta.ec50 , #> # eta.kout , eta.e0 , cp , depot , gut , #> # center , effect , ktr , ka , cl , v , #> # emax , ec50 , kout , e0 , DCP , PD , #> # kin , tad , dosenum "},{"path":"/articles/running-nonmem.html","id":"optional-step-3-use-nlmixr2-to-help-understand-why-nonmem-failed","dir":"Articles","previous_headings":"","what":"Optional Step 3: Use nlmixr2 to help understand why NONMEM failed","title":"Running NONMEM with nlmixr2","text":"Since nlmixr2 fit, can interesting things fit couldn’t NONMEM even another translator. example, wanted add covariance step can getVarCov(): nlmixr2 generous constitutes covariance step. r,s covariance matrix “” successful covariance step focei, system fall back methods necessary. covariance matrix r,s, regarded caution, can still give us clues things working NONMEM. examining fit, can see shrinkage high temax, tktr tka, dropped, making things likely converge NONMEM.","code":"getVarCov(f) #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → calculate jacobian #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate sensitivities #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate ∂(f)/∂(η) #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → finding duplicate expressions in inner model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in inner model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → finding duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling inner model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → finding duplicate expressions in FD model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in FD model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling EBE model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → compiling events FD model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> calculating covariance matrix #> [====|====|====|====|====|====|====|====|====|====] 0:00:08 #> Warning in foceiFitCpp_(.ret): using R matrix to calculate covariance, can #> check sandwich or S matrix with $covRS and $covS #> Warning in foceiFitCpp_(.ret): gradient problems with covariance; see #> $scaleInfo #> → compress origData in nlmixr2 object, save 27560 #> Updated original fit object f #> tktr tka tcl tv temax #> tktr 1.821078e-02 -1.512272e-02 -2.550343e-05 3.216116e-04 0.0015410335 #> tka -1.512272e-02 1.815814e-02 -1.992622e-05 3.175474e-04 0.0010345827 #> tcl -2.550343e-05 -1.992622e-05 2.477225e-04 1.181659e-05 -0.0008009162 #> tv 3.216116e-04 3.175474e-04 1.181659e-05 3.184497e-04 0.0010914727 #> temax 1.541033e-03 1.034583e-03 -8.009162e-04 1.091473e-03 7.5815740647 #> tec50 1.410716e-04 1.273505e-04 -3.578298e-04 1.229707e-04 0.0483191718 #> tkout 1.023011e-04 1.011022e-04 -9.757882e-05 1.188260e-04 -0.0189641465 #> te0 1.310259e-05 1.399880e-05 -9.833068e-06 1.232683e-05 -0.0004365713 #> tec50 tkout te0 #> tktr 0.0001410716 1.023011e-04 1.310259e-05 #> tka 0.0001273505 1.011022e-04 1.399880e-05 #> tcl -0.0003578298 -9.757882e-05 -9.833068e-06 #> tv 0.0001229707 1.188260e-04 1.232683e-05 #> temax 0.0483191718 -1.896415e-02 -4.365713e-04 #> tec50 0.0018345990 1.544065e-04 -1.357629e-04 #> tkout 0.0001544065 6.320302e-04 5.220487e-05 #> te0 -0.0001357629 5.220487e-05 8.843897e-05"},{"path":"/articles/running-nonmem.html","id":"optional-step-4-use-model-piping-to-get-a-successful-nonmem-run","dir":"Articles","previous_headings":"","what":"Optional Step 4: Use model piping to get a successful NONMEM run","title":"Running NONMEM with nlmixr2","text":"use model piping remove parameters, new run start last model’s best estimates (saving bunch model development time). case, specify output directory pk.turnover.emax4 control get following: can see NONMEM run now successful validates rxode2 model : One thing emphasize: unlike translators, know immediately translation model validate. Hence can start process confidence - know immediately something wrong. related converting NONMEM nlmixr2 fit. Since nlmixr2 object easy perform VPC (true NONMEM models):","code":"f2 <- f %>% model(ktr <- exp(tktr)) %>% model(ka <- exp(tka)) %>% model(emax = expit(temax)) %>% nlmixr(data=nlmixr2data::warfarin, est=\"nonmem\", control=nonmemControl(readRounding=FALSE, modelName=\"pk.turnover.emax4\")) #> ! remove between subject variability `eta.ktr` #> ! remove between subject variability `eta.ka` #> ! remove between subject variability `eta.emax` #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → finding duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|==== #> ====|====] 0:00:00 #> → optimizing duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling EBE model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → Calculating residuals/tables #> ✔ done #> → compress origData in nlmixr2 object, save 27560 #> → compress parHistData in nlmixr2 object, save 8800 f2 #> ── nlmixr² nonmem ver 7.4.3 ── #> #> OBJF AIC BIC Log-likelihood Condition#(Cov) #> nonmem focei 1418.923 2338.618 2405.498 -1153.309 1.852796e+16 #> Condition#(Cor) #> nonmem focei 18934770 #> #> ── Time (sec f2$time): ── #> #> setup table compress NONMEM #> elapsed 0.004274 0.047 0.013 505.59 #> #> ── Population Parameters (f2$parFixed or f2$parFixedDf): ── #> #> Est. SE %RSE Back-transformed(95%CI) BSV(CV%) #> tktr 6.24e-07 9.05e-05 1.45e+04 1 (1, 1) #> tka -3.57e-06 0.000153 4.29e+03 1 (1, 1) #> tcl -1.99 0.0639 3.2 0.136 (0.12, 0.154) 27.6 #> tv 2.05 2.66 130 7.76 (0.042, 1.44e+03) 23.6 #> prop.err 0.161 0.161 #> pkadd.err 0.571 0.571 #> temax 9.98 4.96 49.7 1 (0.565, 1) #> tec50 0.131 1.61 1.23e+03 1.14 (0.0489, 26.6) 43.6 #> tkout -2.96 28.3 954 0.0517 (4.63e-26, 5.77e+22) 8.63 #> te0 4.57 0.411 9 96.7 (43.2, 217) 5.19 #> pdadd.err 3.59 3.59 #> Shrink(SD)% #> tktr #> tka #> tcl 3.19% #> tv 10.7% #> prop.err #> pkadd.err #> temax #> tec50 7.12% #> tkout 33.8% #> te0 17.2% #> pdadd.err #> #> Covariance Type (f2$covMethod): nonmem.r,s #> No correlations in between subject variability (BSV) matrix #> Full BSV covariance (f2$omega) or correlation (f2$omegaR; diagonals=SDs) #> Distribution stats (mean/skewness/kurtosis/p-value) available in f2$shrink #> Censoring (f2$censInformation): No censoring #> Minimization message (f2$message): #> #> #> WARNINGS AND ERRORS (IF ANY) FOR PROBLEM 1 #> #> (WARNING 2) NM-TRAN INFERS THAT THE DATA ARE POPULATION. #> #> #> 0MINIMIZATION SUCCESSFUL #> HOWEVER, PROBLEMS OCCURRED WITH THE MINIMIZATION. #> REGARD THE RESULTS OF THE ESTIMATION STEP CAREFULLY, AND ACCEPT THEM ONLY #> AFTER CHECKING THAT THE COVARIANCE STEP PRODUCES REASONABLE OUTPUT. #> NO. OF FUNCTION EVALUATIONS USED: 2391 #> NO. OF SIG. DIGITS IN FINAL EST.: 4.1 #> #> IPRED relative difference compared to Nonmem IPRED: 0%; 95% percentile: (0%,0%); rtol=6.85e-06 #> PRED relative difference compared to Nonmem PRED: 0%; 95% percentile: (0%,0%); rtol=6.45e-06 #> IPRED absolute difference compared to Nonmem IPRED: 95% percentile: (2.89e-06, 0.000506); atol=7.19e-05 #> PRED absolute difference compared to Nonmem PRED: 95% percentile: (5.14e-07,0.00318); atol=6.45e-06 #> nonmem model: 'pk.turnover.emax4-nonmem/pk.turnover.emax4.nmctl' #> #> ── Fit Data (object f2 is a modified tibble): ── #> # A tibble: 483 × 32 #> ID TIME CMT DV PRED RES IPRED IRES IWRES eta.cl eta.v eta.ec50 #> #> 1 1 0.5 cp 0 1.16 -1.16 0.920 -0.920 -1.56 0.689 0.228 0.160 #> 2 1 1 cp 1.9 3.38 -1.48 2.68 -0.780 -1.09 0.689 0.228 0.160 #> 3 1 2 cp 3.3 7.53 -4.23 5.94 -2.64 -2.36 0.689 0.228 0.160 #> # ℹ 480 more rows #> # ℹ 20 more variables: eta.kout , eta.e0 , cp , depot , #> # gut , center , effect , ktr , ka , cl , #> # v , emax , ec50 , kout , e0 , DCP , PD , #> # kin , tad , dosenum v1s <- vpcPlot(f2, show=list(obs_dv=TRUE), scales=\"free_y\") + ylab(\"Warfarin Cp [mg/L] or PCA\") + xlab(\"Time [h]\") #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ v2s <- vpcPlot(f2, show=list(obs_dv=TRUE), pred_corr = TRUE, scales=\"free_y\") + ylab(\"Prediction Corrected Warfarin Cp [mg/L] or PCA\") + xlab(\"Time [h]\") library() v1s v2s"},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Matthew Fidler. Author, maintainer. Bill Denney. Author. Nook Fulloption. Contributor. goldfish art","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Fidler M, Xiong Y, Schoemaker R, Wilkins J, Trame M, Hooijmaijers R, Post T, Wang W (2023). nlmixr: Nonlinear Mixed Effects Models Population Pharmacokinetics Pharmacodynamics. R package version 0.1.1.9000, https://CRAN.R-project.org/package=nlmixr. Fidler M, Wilkins J, Hooijmaijers R, Post T, Schoemaker R, Trame M, Xiong Y, Wang W (2019). “Nonlinear Mixed-Effects Model Development Simulation Using nlmixr Related R Open-Source Packages.” CPT: Pharmacometrics & Systems Pharmacology, 8(9), 621–633. https://doi.org/10.1002/psp4.12445. Schoemaker R, Fidler M, Laveille C, Wilkins J, Hooijmaijers R, Post T, Trame M, Xiong Y, Wang W (2019). “Performance SAEM FOCEI Algorithms Open-Source, Nonlinear Mixed Effect Modeling Tool nlmixr.” CPT: Pharmacometrics & Systems Pharmacology, 8(12), 923–930. https://doi.org/10.1002/psp4.12471.","code":"@Manual{, title = {{nlmixr}: Nonlinear Mixed Effects Models in Population Pharmacokinetics and Pharmacodynamics}, author = {Matthew Fidler and Yuan Xiong and Rik Schoemaker and Justin Wilkins and Mirjam Trame and Richard Hooijmaijers and Teun Post and Wenping Wang}, year = {2023}, note = {R package version 0.1.1.9000}, url = {https://CRAN.R-project.org/package=nlmixr}, } @Article{, title = {Nonlinear Mixed-Effects Model Development and Simulation Using nlmixr and Related R Open-Source Packages}, author = {Matthew Fidler and Justin Wilkins and Richard Hooijmaijers and Teun Post and Rik Schoemaker and Mirjam Trame and Yuan Xiong and Wenping Wang}, journal = {CPT: Pharmacometrics \\& Systems Pharmacology}, year = {2019}, volume = {8}, pages = {621--633}, number = {9}, month = {sep}, abstract = {nlmixr is a free and open-source R package for fitting nonlinear pharmacokinetic (PK), pharmacodynamic (PD), joint PK-PD, and quantitative systems pharmacology mixed-effects models. Currently, nlmixr is capable of fitting both traditional compartmental PK models as well as more complex models implemented using ordinary differential equations. We believe that, over time, it will become a capable, credible alternative to commercial software tools, such as NONMEM, Monolix, and Phoenix NLME.}, address = {Hoboken}, publisher = {John Wiley and Sons Inc.}, url = {https://doi.org/10.1002/psp4.12445}, } @Article{, title = {Performance of the SAEM and FOCEI Algorithms in the Open-Source, Nonlinear Mixed Effect Modeling Tool nlmixr}, author = {Rik Schoemaker and Matthew Fidler and Christian Laveille and Justin Wilkins and Richard Hooijmaijers and Teun Post and Mirjam Trame and Yuan Xiong and Wenping Wang}, journal = {CPT: Pharmacometrics \\& Systems Pharmacology}, year = {2019}, volume = {8}, pages = {923--930}, number = {12}, month = {dec}, abstract = {The free and open-source package nlmixr implements pharmacometric nonlinear mixed effects model parameter estimation in R. It provides a uniform language to define pharmacometric models using ordinary differential equations. Performances of the stochastic approximation expectation-maximization (SAEM) and first order-conditional estimation with interaction (FOCEI) algorithms in nlmixr were compared with those found in the industry standards, Monolix and NONMEM, using the following two scenarios: a simple model fit to 500 sparsely sampled data sets and a range of more complex compartmental models with linear and nonlinear clearance fit to data sets with rich sampling. Estimation results obtained from nlmixr for FOCEI and SAEM matched the corresponding output from NONMEM/FOCEI and Monolix/SAEM closely both in terms of parameter estimates and associated standard errors. These results indicate that nlmixr may provide a viable alternative to existing tools for pharmacometric parameter estimation.}, url = {https://doi.org/10.1002/psp4.12471}, }"},{"path":"/index.html","id":"babelmixr2","dir":"","previous_headings":"","what":"Use nlmixr2 to Interact with Open Source and Commercial Software","title":"Use nlmixr2 to Interact with Open Source and Commercial Software","text":"goal babelmixr2 convert nlmixr2 syntax commonly used tools.","code":""},{"path":"/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Use nlmixr2 to Interact with Open Source and Commercial Software","text":"can install released version babelmixr2 CRAN : can install r-universe : Otherwise can always install GitHub:","code":"install.packages(\"babelmixr2\") # Download and install babelmixr2 in R install.packages('babelmixr2', repos = c( nlmixr2 = 'https://nlmixr2.r-universe.dev', CRAN = 'https://cloud.r-project.org'))"},{"path":"/index.html","id":"what-you-can-do-with-babelmixr2","dir":"","previous_headings":"","what":"What you can do with babelmixr2","title":"Use nlmixr2 to Interact with Open Source and Commercial Software","text":"Babelmixr2 can help : Running nlmixr2 model commercial nonlinear mixed effects modeling tool like NONMEM Monolix Convert NONMEM model nlmixr2 model (conjunction nonmem2rx) Calculate scaling factors automatically add initial conditions based non-compartmental analysis (using PKNCA)","code":""},{"path":"/index.html","id":"monolix-setup","dir":"","previous_headings":"","what":"Monolix Setup","title":"Use nlmixr2 to Interact with Open Source and Commercial Software","text":"required, can get/install R ‘lixoftConnectors’ package ‘Monolix’ installation, described following url https://monolix.lixoft.com/monolix-api/lixoftconnectors_installation/. ‘lixoftConnectors’ available, R can run ‘Monolix’ directly instead using command line.","code":""},{"path":"/index.html","id":"example","dir":"","previous_headings":"","what":"Example","title":"Use nlmixr2 to Interact with Open Source and Commercial Software","text":"installed, use standard interface, can convert Monolix , can convert NONMEM ","code":"mod <- nlmixr(nlmixrFun, nlmmixrData, est=\"monolix\") mod <- nlmixr(nlmixrFun, nlmmixrData, est=\"nonmem\")"},{"path":"/reference/as.nlmixr2.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert an object to a nlmixr2 fit object — as.nlmixr2","title":"Convert an object to a nlmixr2 fit object — as.nlmixr2","text":"Convert object nlmixr2 fit object","code":""},{"path":"/reference/as.nlmixr2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert an object to a nlmixr2 fit object — as.nlmixr2","text":"","code":"as.nlmixr2( x, ..., table = nlmixr2est::tableControl(), rxControl = rxode2::rxControl() ) as.nlmixr( x, ..., table = nlmixr2est::tableControl(), rxControl = rxode2::rxControl() )"},{"path":"/reference/as.nlmixr2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert an object to a nlmixr2 fit object — as.nlmixr2","text":"x Object convert ... arguments table `nlmixr2est::tableControl()` options rxControl `rxode2::rxControl()` options, generally needed `addl` doses handled translation","code":""},{"path":"/reference/as.nlmixr2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert an object to a nlmixr2 fit object — as.nlmixr2","text":"nlmixr2 fit object","code":""},{"path":"/reference/as.nlmixr2.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convert an object to a nlmixr2 fit object — as.nlmixr2","text":"Matthew L. Fidler","code":""},{"path":"/reference/as.nlmixr2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert an object to a nlmixr2 fit object — as.nlmixr2","text":"","code":"# \\donttest{ # First read in the model (but without residuals) mod <- nonmem2rx(system.file(\"mods/cpt/runODE032.ctl\", package=\"nonmem2rx\"), determineError=FALSE, lst=\".res\", save=FALSE) #> ℹ getting information from '/home/runner/work/_temp/Library/nonmem2rx/mods/cpt/runODE032.ctl' #> ℹ reading in xml file #> ℹ done #> ℹ reading in phi file #> ℹ done #> ℹ reading in lst file #> ℹ abbreviated list parsing #> ℹ done #> ℹ done #> ℹ splitting control stream by records #> ℹ done #> ℹ Processing record $INPUT #> ℹ Processing record $MODEL #> ℹ Processing record $THETA #> ℹ Processing record $OMEGA #> ℹ Processing record $SIGMA #> ℹ Processing record $PROBLEM #> ℹ Processing record $DATA #> ℹ Processing record $SUBROUTINES #> ℹ Processing record $PK #> ℹ Processing record $DES #> ℹ Processing record $ERROR #> ℹ Processing record $ESTIMATION #> ℹ Ignore record $ESTIMATION #> ℹ Processing record $COVARIANCE #> ℹ Ignore record $COVARIANCE #> ℹ Processing record $TABLE #> ℹ change initial estimate of `theta1` to `1.37034036528946` #> ℹ change initial estimate of `theta2` to `4.19814911033061` #> ℹ change initial estimate of `theta3` to `1.38003493562413` #> ℹ change initial estimate of `theta4` to `3.87657341967489` #> ℹ change initial estimate of `theta5` to `0.196446108190896` #> ℹ change initial estimate of `eta1` to `0.101251418415006` #> ℹ change initial estimate of `eta2` to `0.0993872449483344` #> ℹ change initial estimate of `eta3` to `0.101302674763154` #> ℹ change initial estimate of `eta4` to `0.0730497519364148` #> ℹ read in nonmem input data (for model validation): /home/runner/work/_temp/Library/nonmem2rx/mods/cpt/Bolus_2CPT.csv #> ℹ ignoring lines that begin with a letter (IGNORE=@)' #> ℹ applying names specified by $INPUT #> ℹ subsetting accept/ignore filters code: .data[-which((.data$SD == 0)),] #> ℹ done #> #> #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ℹ read in nonmem IPRED data (for model validation): /home/runner/work/_temp/Library/nonmem2rx/mods/cpt/runODE032.csv #> ℹ done #> ℹ changing most variables to lower case #> ℹ done #> ℹ replace theta names #> ℹ done #> ℹ replace eta names #> ℹ done (no labels) #> ℹ renaming compartments #> ℹ done #> #> #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ℹ solving ipred problem #> ℹ done #> ℹ solving pred problem #> ℹ done # define the model with residuals (and change the name of the # parameters) In this step you need to be careful to not change the # estimates and make sure the residual estimates are correct (could # have to change var to sd). mod2 <-function() { ini({ lcl <- 1.37034036528946 lvc <- 4.19814911033061 lq <- 1.38003493562413 lvp <- 3.87657341967489 RSV <- c(0, 0.196446108190896, 1) eta.cl ~ 0.101251418415006 eta.v ~ 0.0993872449483344 eta.q ~ 0.101302674763154 eta.v2 ~ 0.0730497519364148 }) model({ cmt(CENTRAL) cmt(PERI) cl <- exp(lcl + eta.cl) v <- exp(lvc + eta.v) q <- exp(lq + eta.q) v2 <- exp(lvp + eta.v2) v1 <- v scale1 <- v k21 <- q/v2 k12 <- q/v d/dt(CENTRAL) <- k21 * PERI - k12 * CENTRAL - cl * CENTRAL/v1 d/dt(PERI) <- -k21 * PERI + k12 * CENTRAL f <- CENTRAL/scale1 f ~ prop(RSV) }) } # now we create another nonmem2rx object that validates the model above: new <- as.nonmem2rx(mod2, mod) #> #> #> #> #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ℹ solving ipred problem #> ℹ done #> ℹ solving pred problem #> ℹ done # once that is done, you can translate to a full nlmixr2 fit (if you wish) fit <- as.nlmixr2(new) #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → finding duplicate expressions in EBE model... #> → optimizing duplicate expressions in EBE model... #> → compiling EBE model... #> #> #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> rxode2 2.0.14.9000 using 1 threads (see ?getRxThreads) #> no cache: create with `rxCreateCache()` #> → Calculating residuals/tables #> ✔ done #> → compress origData in nlmixr2 object, save 204016 #> → compress parHistData in nlmixr2 object, save 2176 print(fit) #> ── nlmixr² nonmem2rx reading NONMEM ver 7.4.3 ── #> #> OBJF AIC BIC Log-likelihood Condition#(Cov) #> nonmem2rx 15977.28 20185.64 20237.23 -10083.82 335.4129 #> Condition#(Cor) #> nonmem2rx 2.096559 #> #> ── Time (sec $time): ── #> #> setup table compress NONMEM as.nlmixr2 #> elapsed 0.046263 0.071 0.023 100.95 3.472 #> #> ── Population Parameters ($parFixed or $parFixedDf): ── #> #> Est. SE %RSE Back-transformed(95%CI) BSV(CV%) Shrink(SD)% #> lcl 1.37 0.0298 2.17 3.94 (3.71, 4.17) 32.6 1.94% #> lvc 4.2 0.0295 0.703 66.6 (62.8, 70.5) 32.3 2.46% #> lq 1.38 0.0547 3.96 3.98 (3.57, 4.42) 32.7 40.5% #> lvp 3.88 0.0348 0.899 48.3 (45.1, 51.7) 27.5 28.4% #> RSV 0.196 0.196 #> #> Covariance Type ($covMethod): nonmem2rx #> No correlations in between subject variability (BSV) matrix #> Full BSV covariance ($omega) or correlation ($omegaR; diagonals=SDs) #> Distribution stats (mean/skewness/kurtosis/p-value) available in $shrink #> Censoring ($censInformation): No censoring #> Minimization message ($message): #> #> #> WARNINGS AND ERRORS (IF ANY) FOR PROBLEM 1 #> #> (WARNING 2) NM-TRAN INFERS THAT THE DATA ARE POPULATION. #> #> #> 0MINIMIZATION SUCCESSFUL #> NO. OF FUNCTION EVALUATIONS USED: 320 #> NO. OF SIG. DIGITS IN FINAL EST.: 2.5 #> #> IPRED relative difference compared to Nonmem IPRED: 0%; 95% percentile: (0%,0%); rtol=6.43e-06 #> PRED relative difference compared to Nonmem PRED: 0%; 95% percentile: (0%,0%); rtol=6.41e-06 #> IPRED absolute difference compared to Nonmem IPRED: 95% percentile: (2.25e-05, 0.0418); atol=0.00167 #> PRED absolute difference compared to Nonmem PRED: 95% percentile: (1.41e-07,0.00382); atol=6.41e-06 #> nonmem2rx model file: '/home/runner/work/_temp/Library/nonmem2rx/mods/cpt/runODE032.ctl' #> #> ── Fit Data (object is a modified tibble): ── #> # A tibble: 2,280 × 25 #> ID TIME DV PRED RES IPRED IRES IWRES eta.cl eta.v eta.q eta.v2 #> #> 1 1 0.25 1041. 1750. -710. 1215. -175. -0.732 -0.144 0.375 0.0650 0.241 #> 2 1 0.5 1629 1700. -70.8 1192. 437. 1.87 -0.144 0.375 0.0650 0.241 #> 3 1 0.75 878. 1651. -774. 1169. -291. -1.27 -0.144 0.375 0.0650 0.241 #> # ℹ 2,277 more rows #> # ℹ 13 more variables: f , CENTRAL , PERI , cl , v , #> # q , v2 , v1 , scale1 , k21 , k12 , tad , #> # dosenum # }"},{"path":"/reference/bblDatToMonolix.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","title":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","text":"Convert nlmixr2-compatible data formats (possible)","code":""},{"path":"/reference/bblDatToMonolix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","text":"","code":"bblDatToMonolix( model, data, table = nlmixr2est::tableControl(), rxControl = rxode2::rxControl(), env = NULL ) bblDatToNonmem( model, data, table = nlmixr2est::tableControl(), rxControl = rxode2::rxControl(), env = NULL ) bblDatToRxode( model, data, table = nlmixr2est::tableControl(), rxControl = rxode2::rxControl(), env = NULL ) bblDatToMrgsolve( model, data, table = nlmixr2est::tableControl(), rxControl = rxode2::rxControl(), env = NULL ) bblDatToPknca( model, data, table = nlmixr2est::tableControl(), rxControl = rxode2::rxControl(), env = NULL )"},{"path":"/reference/bblDatToMonolix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","text":"model rxode2 model conversion data Input dataset. table table control; mostly figure additional columns keep. rxControl rxode2 control options; figure handle addl dosing information. env `NULL` (default) nothing done. environment, function `nlmixr2est::.foceiPreProcessData(data, env, model, rxControl)` called provided environment.","code":""},{"path":"/reference/bblDatToMonolix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","text":"function `bblDatToMonolix()` return list : - Monolix compatible dataset ($monolix) - Monolix ADM information ($adm) function `nlmixrDataToNonmem()` return dataset compatible NONMEM. function `nlmixrDataToMrgsolve()` return dataset compatible `mrgsolve`. Unlike NONMEM, supports replacement events `evid=8` (note `rxode2` replacement `evid` `5`). function `nlmixrDataToRxode()` normalize dataset use newer `evid` definitions closer NONMEM instead classic definitions used lower level","code":""},{"path":"/reference/bblDatToMonolix.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","text":"Matthew L. Fidler","code":""},{"path":"/reference/bblDatToMonolix.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","text":"","code":"pk.turnover.emax3 <- function() { ini({ tktr <- log(1) tka <- log(1) tcl <- log(0.1) tv <- log(10) ## eta.ktr ~ 1 eta.ka ~ 1 eta.cl ~ 2 eta.v ~ 1 prop.err <- 0.1 pkadd.err <- 0.1 ## temax <- logit(0.8) tec50 <- log(0.5) tkout <- log(0.05) te0 <- log(100) ## eta.emax ~ .5 eta.ec50 ~ .5 eta.kout ~ .5 eta.e0 ~ .5 ## pdadd.err <- 10 }) model({ ktr <- exp(tktr + eta.ktr) ka <- exp(tka + eta.ka) cl <- exp(tcl + eta.cl) v <- exp(tv + eta.v) emax = expit(temax+eta.emax) ec50 = exp(tec50 + eta.ec50) kout = exp(tkout + eta.kout) e0 = exp(te0 + eta.e0) ## DCP = center/v PD=1-emax*DCP/(ec50+DCP) ## effect(0) = e0 kin = e0*kout ## d/dt(depot) = -ktr * depot d/dt(gut) = ktr * depot -ka * gut d/dt(center) = ka * gut - cl / v * center d/dt(effect) = kin*PD -kout*effect ## cp = center / v cp ~ prop(prop.err) + add(pkadd.err) effect ~ add(pdadd.err) | pca }) } bblDatToMonolix(pk.turnover.emax3, nlmixr2data::warfarin) #> #> #> $monolix #> ID TIME EVID AMT II DV ADM YTYPE SS nlmixrRowNums #> 1 1 0.0 1 100.0 0 NA 1 0 0 1 #> 2 1 0.5 0 NA 0 0.0 0 1 0 2 #> 3 1 1.0 0 NA 0 1.9 0 1 0 3 #> 4 1 2.0 0 NA 0 3.3 0 1 0 4 #> 5 1 3.0 0 NA 0 6.6 0 1 0 5 #> 6 1 6.0 0 NA 0 9.1 0 1 0 6 #> 7 1 9.0 0 NA 0 10.8 0 1 0 7 #> 8 1 12.0 0 NA 0 8.6 0 1 0 8 #> 9 1 24.0 0 NA 0 5.6 0 1 0 9 #> 10 1 24.0 0 NA 0 44.0 0 2 0 10 #> 11 1 36.0 0 NA 0 4.0 0 1 0 11 #> 12 1 36.0 0 NA 0 27.0 0 2 0 12 #> 13 1 48.0 0 NA 0 2.7 0 1 0 13 #> 14 1 48.0 0 NA 0 28.0 0 2 0 14 #> 15 1 72.0 0 NA 0 0.8 0 1 0 15 #> 16 1 72.0 0 NA 0 31.0 0 2 0 16 #> 17 1 96.0 0 NA 0 60.0 0 2 0 17 #> 18 1 120.0 0 NA 0 65.0 0 2 0 18 #> 19 1 144.0 0 NA 0 71.0 0 2 0 19 #> 20 2 0.0 1 100.0 0 NA 1 0 0 20 #> 21 2 0.0 0 NA 0 100.0 0 2 0 21 #> 22 2 24.0 0 NA 0 9.2 0 1 0 22 #> 23 2 24.0 0 NA 0 49.0 0 2 0 23 #> 24 2 36.0 0 NA 0 8.5 0 1 0 24 #> 25 2 36.0 0 NA 0 32.0 0 2 0 25 #> 26 2 48.0 0 NA 0 6.4 0 1 0 26 #> 27 2 48.0 0 NA 0 26.0 0 2 0 27 #> 28 2 72.0 0 NA 0 4.8 0 1 0 28 #> 29 2 72.0 0 NA 0 22.0 0 2 0 29 #> 30 2 96.0 0 NA 0 3.1 0 1 0 30 #> 31 2 96.0 0 NA 0 28.0 0 2 0 31 #> 32 2 120.0 0 NA 0 2.5 0 1 0 32 #> 33 2 120.0 0 NA 0 33.0 0 2 0 33 #> 34 3 0.0 1 100.0 0 NA 1 0 0 34 #> 35 3 0.0 0 NA 0 100.0 0 2 0 35 #> 36 3 0.5 0 NA 0 0.0 0 1 0 36 #> 37 3 2.0 0 NA 0 8.4 0 1 0 37 #> 38 3 3.0 0 NA 0 9.7 0 1 0 38 #> 39 3 6.0 0 NA 0 9.8 0 1 0 39 #> 40 3 12.0 0 NA 0 11.0 0 1 0 40 #> 41 3 24.0 0 NA 0 8.3 0 1 0 41 #> 42 3 24.0 0 NA 0 46.0 0 2 0 42 #> 43 3 36.0 0 NA 0 7.7 0 1 0 43 #> 44 3 36.0 0 NA 0 22.0 0 2 0 44 #> 45 3 48.0 0 NA 0 6.3 0 1 0 45 #> 46 3 48.0 0 NA 0 19.0 0 2 0 46 #> 47 3 72.0 0 NA 0 4.1 0 1 0 47 #> 48 3 72.0 0 NA 0 20.0 0 2 0 48 #> 49 3 96.0 0 NA 0 3.0 0 1 0 49 #> 50 3 96.0 0 NA 0 42.0 0 2 0 50 #> 51 3 120.0 0 NA 0 1.4 0 1 0 51 #> 52 3 120.0 0 NA 0 49.0 0 2 0 52 #> 53 3 144.0 0 NA 0 54.0 0 2 0 53 #> 54 4 0.0 1 120.0 0 NA 1 0 0 54 #> 55 4 0.0 0 NA 0 100.0 0 2 0 55 #> 56 4 3.0 0 NA 0 12.0 0 1 0 56 #> 57 4 6.0 0 NA 0 13.2 0 1 0 57 #> 58 4 9.0 0 NA 0 14.4 0 1 0 58 #> 59 4 24.0 0 NA 0 9.6 0 1 0 59 #> 60 4 24.0 0 NA 0 30.0 0 2 0 60 #> 61 4 36.0 0 NA 0 8.2 0 1 0 61 #> 62 4 36.0 0 NA 0 24.0 0 2 0 62 #> 63 4 48.0 0 NA 0 7.8 0 1 0 63 #> 64 4 48.0 0 NA 0 13.0 0 2 0 64 #> 65 4 72.0 0 NA 0 5.8 0 1 0 65 #> 66 4 72.0 0 NA 0 9.0 0 2 0 66 #> 67 4 96.0 0 NA 0 4.3 0 1 0 67 #> 68 4 96.0 0 NA 0 9.0 0 2 0 68 #> 69 4 120.0 0 NA 0 3.0 0 1 0 69 #> 70 4 120.0 0 NA 0 11.0 0 2 0 70 #> 71 4 144.0 0 NA 0 12.0 0 2 0 71 #> 72 5 0.0 1 60.0 0 NA 1 0 0 72 #> 73 5 0.0 0 NA 0 82.0 0 2 0 73 #> 74 5 3.0 0 NA 0 11.1 0 1 0 74 #> 75 5 6.0 0 NA 0 11.9 0 1 0 75 #> 76 5 9.0 0 NA 0 9.8 0 1 0 76 #> 77 5 12.0 0 NA 0 11.0 0 1 0 77 #> 78 5 24.0 0 NA 0 8.5 0 1 0 78 #> 79 5 24.0 0 NA 0 43.0 0 2 0 79 #> 80 5 36.0 0 NA 0 7.6 0 1 0 80 #> 81 5 36.0 0 NA 0 25.0 0 2 0 81 #> 82 5 48.0 0 NA 0 5.4 0 1 0 82 #> 83 5 48.0 0 NA 0 18.0 0 2 0 83 #> 84 5 72.0 0 NA 0 4.5 0 1 0 84 #> 85 5 72.0 0 NA 0 17.0 0 2 0 85 #> 86 5 96.0 0 NA 0 3.3 0 1 0 86 #> 87 5 96.0 0 NA 0 23.0 0 2 0 87 #> 88 5 120.0 0 NA 0 2.3 0 1 0 88 #> 89 5 120.0 0 NA 0 29.0 0 2 0 89 #> 90 5 144.0 0 NA 0 41.0 0 2 0 90 #> 91 6 0.0 1 113.0 0 NA 1 0 0 91 #> 92 6 0.0 0 NA 0 100.0 0 2 0 92 #> 93 6 6.0 0 NA 0 8.6 0 1 0 93 #> 94 6 12.0 0 NA 0 8.6 0 1 0 94 #> 95 6 24.0 0 NA 0 7.0 0 1 0 95 #> 96 6 24.0 0 NA 0 34.0 0 2 0 96 #> 97 6 36.0 0 NA 0 5.7 0 1 0 97 #> 98 6 36.0 0 NA 0 23.0 0 2 0 98 #> 99 6 48.0 0 NA 0 4.7 0 1 0 99 #> 100 6 48.0 0 NA 0 20.0 0 2 0 100 #> 101 6 72.0 0 NA 0 3.3 0 1 0 101 #> 102 6 72.0 0 NA 0 16.0 0 2 0 102 #> 103 6 96.0 0 NA 0 2.3 0 1 0 103 #> 104 6 96.0 0 NA 0 17.0 0 2 0 104 #> 105 6 120.0 0 NA 0 1.7 0 1 0 105 #> 106 6 120.0 0 NA 0 18.0 0 2 0 106 #> 107 6 144.0 0 NA 0 25.0 0 2 0 107 #> 108 7 0.0 1 90.0 0 NA 1 0 0 108 #> 109 7 3.0 0 NA 0 13.4 0 1 0 109 #> 110 7 6.0 0 NA 0 12.4 0 1 0 110 #> 111 7 9.0 0 NA 0 12.7 0 1 0 111 #> 112 7 12.0 0 NA 0 8.8 0 1 0 112 #> 113 7 24.0 0 NA 0 6.1 0 1 0 113 #> 114 7 24.0 0 NA 0 36.0 0 2 0 114 #> 115 7 36.0 0 NA 0 3.5 0 1 0 115 #> 116 7 36.0 0 NA 0 33.0 0 2 0 116 #> 117 7 48.0 0 NA 0 1.8 0 1 0 117 #> 118 7 48.0 0 NA 0 28.0 0 2 0 118 #> 119 7 72.0 0 NA 0 1.5 0 1 0 119 #> 120 7 72.0 0 NA 0 52.0 0 2 0 120 #> 121 7 96.0 0 NA 0 1.0 0 1 0 121 #> 122 7 96.0 0 NA 0 80.0 0 2 0 122 #> 123 7 120.0 0 NA 0 90.0 0 2 0 123 #> 124 7 144.0 0 NA 0 100.0 0 2 0 124 #> 125 8 0.0 1 135.0 0 NA 1 0 0 125 #> 126 8 0.0 0 NA 0 88.0 0 2 0 126 #> 127 8 2.0 0 NA 0 17.6 0 1 0 127 #> 128 8 3.0 0 NA 0 17.3 0 1 0 128 #> 129 8 6.0 0 NA 0 15.0 0 1 0 129 #> 130 8 9.0 0 NA 0 15.0 0 1 0 130 #> 131 8 12.0 0 NA 0 12.4 0 1 0 131 #> 132 8 24.0 0 NA 0 7.9 0 1 0 132 #> 133 8 24.0 0 NA 0 35.0 0 2 0 133 #> 134 8 36.0 0 NA 0 7.9 0 1 0 134 #> 135 8 36.0 0 NA 0 20.0 0 2 0 135 #> 136 8 48.0 0 NA 0 5.1 0 1 0 136 #> 137 8 48.0 0 NA 0 12.0 0 2 0 137 #> 138 8 72.0 0 NA 0 3.6 0 1 0 138 #> 139 8 72.0 0 NA 0 16.0 0 2 0 139 #> 140 8 96.0 0 NA 0 2.4 0 1 0 140 #> 141 8 96.0 0 NA 0 23.0 0 2 0 141 #> 142 8 120.0 0 NA 0 2.0 0 1 0 142 #> 143 8 120.0 0 NA 0 36.0 0 2 0 143 #> 144 8 144.0 0 NA 0 48.0 0 2 0 144 #> 145 9 0.0 1 75.0 0 NA 1 0 0 145 #> 146 9 0.0 0 NA 0 92.0 0 2 0 146 #> 147 9 0.5 0 NA 0 0.0 0 1 0 147 #> 148 9 1.0 0 NA 0 1.0 0 1 0 148 #> 149 9 2.0 0 NA 0 4.6 0 1 0 149 #> 150 9 3.0 0 NA 0 12.7 0 1 0 150 #> 151 9 3.0 0 NA 0 8.0 0 1 0 151 #> 152 9 6.0 0 NA 0 12.7 0 1 0 152 #> 153 9 6.0 0 NA 0 11.5 0 1 0 153 #> 154 9 9.0 0 NA 0 12.9 0 1 0 154 #> 155 9 9.0 0 NA 0 11.4 0 1 0 155 #> 156 9 12.0 0 NA 0 11.4 0 1 0 156 #> 157 9 12.0 0 NA 0 11.0 0 1 0 157 #> 158 9 24.0 0 NA 0 9.1 0 1 0 158 #> 159 9 24.0 0 NA 0 33.0 0 2 0 159 #> 160 9 36.0 0 NA 0 8.2 0 1 0 160 #> 161 9 36.0 0 NA 0 22.0 0 2 0 161 #> 162 9 48.0 0 NA 0 5.9 0 1 0 162 #> 163 9 48.0 0 NA 0 16.0 0 2 0 163 #> 164 9 72.0 0 NA 0 3.6 0 1 0 164 #> 165 9 72.0 0 NA 0 18.0 0 2 0 165 #> 166 9 96.0 0 NA 0 1.7 0 1 0 166 #> 167 9 96.0 0 NA 0 32.0 0 2 0 167 #> 168 9 120.0 0 NA 0 1.1 0 1 0 168 #> 169 9 120.0 0 NA 0 30.0 0 2 0 169 #> 170 9 144.0 0 NA 0 45.0 0 2 0 170 #> 171 10 0.0 1 105.0 0 NA 1 0 0 171 #> 172 10 0.0 0 NA 0 90.0 0 2 0 172 #> 173 10 24.0 0 NA 0 8.6 0 1 0 173 #> 174 10 24.0 0 NA 0 39.0 0 2 0 174 #> 175 10 36.0 0 NA 0 8.0 0 1 0 175 #> 176 10 36.0 0 NA 0 22.0 0 2 0 176 #> 177 10 48.0 0 NA 0 6.0 0 1 0 177 #> 178 10 48.0 0 NA 0 17.0 0 2 0 178 #> 179 10 72.0 0 NA 0 4.4 0 1 0 179 #> 180 10 72.0 0 NA 0 17.0 0 2 0 180 #> 181 10 96.0 0 NA 0 3.6 0 1 0 181 #> 182 10 96.0 0 NA 0 22.0 0 2 0 182 #> 183 10 120.0 0 NA 0 2.8 0 1 0 183 #> 184 10 120.0 0 NA 0 25.0 0 2 0 184 #> 185 10 144.0 0 NA 0 33.0 0 2 0 185 #> 186 11 0.0 1 123.0 0 NA 1 0 0 186 #> 187 11 0.0 0 NA 0 100.0 0 2 0 187 #> 188 11 1.5 0 NA 0 11.4 0 1 0 188 #> 189 11 3.0 0 NA 0 15.4 0 1 0 189 #> 190 11 6.0 0 NA 0 17.5 0 1 0 190 #> 191 11 12.0 0 NA 0 14.0 0 1 0 191 #> 192 11 24.0 0 NA 0 9.0 0 1 0 192 #> 193 11 24.0 0 NA 0 37.0 0 2 0 193 #> 194 11 36.0 0 NA 0 8.9 0 1 0 194 #> 195 11 36.0 0 NA 0 24.0 0 2 0 195 #> 196 11 48.0 0 NA 0 6.6 0 1 0 196 #> 197 11 48.0 0 NA 0 14.0 0 2 0 197 #> 198 11 72.0 0 NA 0 4.2 0 1 0 198 #> 199 11 72.0 0 NA 0 11.0 0 2 0 199 #> 200 11 96.0 0 NA 0 3.6 0 1 0 200 #> 201 11 96.0 0 NA 0 14.0 0 2 0 201 #> 202 11 120.0 0 NA 0 2.6 0 1 0 202 #> 203 11 120.0 0 NA 0 23.0 0 2 0 203 #> 204 11 144.0 0 NA 0 33.0 0 2 0 204 #> 205 12 0.0 1 113.0 0 NA 1 0 0 205 #> 206 12 0.0 0 NA 0 85.0 0 2 0 206 #> 207 12 1.5 0 NA 0 0.6 0 1 0 207 #> 208 12 3.0 0 NA 0 2.8 0 1 0 208 #> 209 12 6.0 0 NA 0 13.8 0 1 0 209 #> 210 12 9.0 0 NA 0 15.0 0 1 0 210 #> 211 12 24.0 0 NA 0 10.5 0 1 0 211 #> 212 12 24.0 0 NA 0 25.0 0 2 0 212 #> 213 12 36.0 0 NA 0 9.1 0 1 0 213 #> 214 12 36.0 0 NA 0 15.0 0 2 0 214 #> 215 12 48.0 0 NA 0 6.6 0 1 0 215 #> 216 12 48.0 0 NA 0 11.0 0 2 0 216 #> 217 12 72.0 0 NA 0 4.9 0 1 0 217 #> 218 12 96.0 0 NA 0 2.4 0 1 0 218 #> 219 12 120.0 0 NA 0 1.9 0 1 0 219 #> 220 13 0.0 1 113.0 0 NA 1 0 0 220 #> 221 13 0.0 0 NA 0 88.0 0 2 0 221 #> 222 13 1.5 0 NA 0 3.6 0 1 0 222 #> 223 13 3.0 0 NA 0 12.9 0 1 0 223 #> 224 13 6.0 0 NA 0 12.9 0 1 0 224 #> 225 13 9.0 0 NA 0 10.2 0 1 0 225 #> 226 13 24.0 0 NA 0 6.4 0 1 0 226 #> 227 13 24.0 0 NA 0 41.0 0 2 0 227 #> 228 13 36.0 0 NA 0 6.9 0 1 0 228 #> 229 13 36.0 0 NA 0 23.0 0 2 0 229 #> 230 13 48.0 0 NA 0 4.5 0 1 0 230 #> 231 13 48.0 0 NA 0 16.0 0 2 0 231 #> 232 13 72.0 0 NA 0 3.2 0 1 0 232 #> 233 13 72.0 0 NA 0 14.0 0 2 0 233 #> 234 13 96.0 0 NA 0 2.4 0 1 0 234 #> 235 13 96.0 0 NA 0 18.0 0 2 0 235 #> 236 13 120.0 0 NA 0 1.3 0 1 0 236 #> 237 13 120.0 0 NA 0 22.0 0 2 0 237 #> 238 13 144.0 0 NA 0 35.0 0 2 0 238 #> 239 14 0.0 1 75.0 0 NA 1 0 0 239 #> 240 14 0.0 0 NA 0 85.0 0 2 0 240 #> 241 14 0.5 0 NA 0 0.0 0 1 0 241 #> 242 14 1.0 0 NA 0 2.7 0 1 0 242 #> 243 14 2.0 0 NA 0 11.6 0 1 0 243 #> 244 14 3.0 0 NA 0 11.6 0 1 0 244 #> 245 14 6.0 0 NA 0 11.3 0 1 0 245 #> 246 14 9.0 0 NA 0 9.7 0 1 0 246 #> 247 14 24.0 0 NA 0 6.5 0 1 0 247 #> 248 14 24.0 0 NA 0 32.0 0 2 0 248 #> 249 14 36.0 0 NA 0 5.2 0 1 0 249 #> 250 14 36.0 0 NA 0 22.0 0 2 0 250 #> 251 14 48.0 0 NA 0 3.6 0 1 0 251 #> 252 14 48.0 0 NA 0 21.0 0 2 0 252 #> 253 14 72.0 0 NA 0 2.4 0 1 0 253 #> 254 14 72.0 0 NA 0 28.0 0 2 0 254 #> 255 14 96.0 0 NA 0 0.9 0 1 0 255 #> 256 14 96.0 0 NA 0 38.0 0 2 0 256 #> 257 14 120.0 0 NA 0 46.0 0 2 0 257 #> 258 14 144.0 0 NA 0 65.0 0 2 0 258 #> 259 15 0.0 1 85.0 0 NA 1 0 0 259 #> 260 15 0.0 0 NA 0 100.0 0 2 0 260 #> 261 15 1.0 0 NA 0 6.6 0 1 0 261 #> 262 15 3.0 0 NA 0 11.9 0 1 0 262 #> 263 15 6.0 0 NA 0 11.7 0 1 0 263 #> 264 15 9.0 0 NA 0 12.2 0 1 0 264 #> 265 15 24.0 0 NA 0 8.1 0 1 0 265 #> 266 15 24.0 0 NA 0 43.0 0 2 0 266 #> 267 15 36.0 0 NA 0 7.4 0 1 0 267 #> 268 15 36.0 0 NA 0 26.0 0 2 0 268 #> 269 15 48.0 0 NA 0 6.8 0 1 0 269 #> 270 15 48.0 0 NA 0 15.0 0 2 0 270 #> 271 15 72.0 0 NA 0 5.3 0 1 0 271 #> 272 15 72.0 0 NA 0 13.0 0 2 0 272 #> 273 15 96.0 0 NA 0 3.0 0 1 0 273 #> 274 15 96.0 0 NA 0 21.0 0 2 0 274 #> 275 15 120.0 0 NA 0 2.0 0 1 0 275 #> 276 15 120.0 0 NA 0 28.0 0 2 0 276 #> 277 15 144.0 0 NA 0 39.0 0 2 0 277 #> 278 16 0.0 1 87.0 0 NA 1 0 0 278 #> 279 16 0.0 0 NA 0 100.0 0 2 0 279 #> 280 16 24.0 0 NA 0 10.4 0 1 0 280 #> 281 16 24.0 0 NA 0 42.0 0 2 0 281 #> 282 16 36.0 0 NA 0 8.9 0 1 0 282 #> 283 16 36.0 0 NA 0 32.0 0 2 0 283 #> 284 16 48.0 0 NA 0 7.0 0 1 0 284 #> 285 16 48.0 0 NA 0 26.0 0 2 0 285 #> 286 16 72.0 0 NA 0 4.4 0 1 0 286 #> 287 16 72.0 0 NA 0 31.0 0 2 0 287 #> 288 16 96.0 0 NA 0 3.2 0 1 0 288 #> 289 16 96.0 0 NA 0 33.0 0 2 0 289 #> 290 16 120.0 0 NA 0 2.4 0 1 0 290 #> 291 16 120.0 0 NA 0 54.0 0 2 0 291 #> 292 17 0.0 1 117.0 0 NA 1 0 0 292 #> 293 17 0.0 0 NA 0 100.0 0 2 0 293 #> 294 17 24.0 0 NA 0 7.6 0 1 0 294 #> 295 17 24.0 0 NA 0 35.0 0 2 0 295 #> 296 17 36.0 0 NA 0 6.4 0 1 0 296 #> 297 17 36.0 0 NA 0 23.0 0 2 0 297 #> 298 17 48.0 0 NA 0 6.0 0 1 0 298 #> 299 17 48.0 0 NA 0 17.0 0 2 0 299 #> 300 17 72.0 0 NA 0 4.0 0 1 0 300 #> 301 17 72.0 0 NA 0 18.0 0 2 0 301 #> 302 17 96.0 0 NA 0 3.1 0 1 0 302 #> 303 17 96.0 0 NA 0 18.0 0 2 0 303 #> 304 17 120.0 0 NA 0 2.0 0 1 0 304 #> 305 17 120.0 0 NA 0 21.0 0 2 0 305 #> 306 18 0.0 1 112.0 0 NA 1 0 0 306 #> 307 18 0.0 0 NA 0 100.0 0 2 0 307 #> 308 18 24.0 0 NA 0 7.6 0 1 0 308 #> 309 18 24.0 0 NA 0 32.0 0 2 0 309 #> 310 18 36.0 0 NA 0 6.6 0 1 0 310 #> 311 18 36.0 0 NA 0 20.0 0 2 0 311 #> 312 18 48.0 0 NA 0 5.4 0 1 0 312 #> 313 18 48.0 0 NA 0 18.0 0 2 0 313 #> 314 18 72.0 0 NA 0 3.4 0 1 0 314 #> 315 18 72.0 0 NA 0 18.0 0 2 0 315 #> 316 18 96.0 0 NA 0 1.2 0 1 0 316 #> 317 18 96.0 0 NA 0 19.0 0 2 0 317 #> 318 18 120.0 0 NA 0 0.9 0 1 0 318 #> 319 18 120.0 0 NA 0 29.0 0 2 0 319 #> 320 19 0.0 1 95.5 0 NA 1 0 0 320 #> 321 19 0.0 0 NA 0 100.0 0 2 0 321 #> 322 19 24.0 0 NA 0 6.6 0 1 0 322 #> 323 19 24.0 0 NA 0 33.0 0 2 0 323 #> 324 19 36.0 0 NA 0 5.3 0 1 0 324 #> 325 19 36.0 0 NA 0 28.0 0 2 0 325 #> 326 19 48.0 0 NA 0 3.6 0 1 0 326 #> 327 19 48.0 0 NA 0 18.0 0 2 0 327 #> 328 19 72.0 0 NA 0 2.7 0 1 0 328 #> 329 19 72.0 0 NA 0 18.0 0 2 0 329 #> 330 19 96.0 0 NA 0 1.4 0 1 0 330 #> 331 19 96.0 0 NA 0 17.0 0 2 0 331 #> 332 19 120.0 0 NA 0 1.1 0 1 0 332 #> 333 19 120.0 0 NA 0 26.0 0 2 0 333 #> 334 20 0.0 1 88.5 0 NA 1 0 0 334 #> 335 20 0.0 0 NA 0 100.0 0 2 0 335 #> 336 20 24.0 0 NA 0 9.6 0 1 0 336 #> 337 20 24.0 0 NA 0 41.0 0 2 0 337 #> 338 20 36.0 0 NA 0 8.0 0 1 0 338 #> 339 20 36.0 0 NA 0 30.0 0 2 0 339 #> 340 20 48.0 0 NA 0 6.6 0 1 0 340 #> 341 20 48.0 0 NA 0 22.0 0 2 0 341 #> 342 20 72.0 0 NA 0 5.6 0 1 0 342 #> 343 20 72.0 0 NA 0 23.0 0 2 0 343 #> 344 20 96.0 0 NA 0 3.5 0 1 0 344 #> 345 20 96.0 0 NA 0 23.0 0 2 0 345 #> 346 20 120.0 0 NA 0 2.3 0 1 0 346 #> 347 20 120.0 0 NA 0 35.0 0 2 0 347 #> 348 21 0.0 1 93.0 0 NA 1 0 0 348 #> 349 21 0.0 0 NA 0 100.0 0 2 0 349 #> 350 21 24.0 0 NA 0 7.3 0 1 0 350 #> 351 21 24.0 0 NA 0 46.0 0 2 0 351 #> 352 21 36.0 0 NA 0 6.1 0 1 0 352 #> 353 21 36.0 0 NA 0 27.0 0 2 0 353 #> 354 21 48.0 0 NA 0 4.3 0 1 0 354 #> 355 21 48.0 0 NA 0 22.0 0 2 0 355 #> 356 21 72.0 0 NA 0 3.2 0 1 0 356 #> 357 21 72.0 0 NA 0 36.0 0 2 0 357 #> 358 21 96.0 0 NA 0 2.3 0 1 0 358 #> 359 21 96.0 0 NA 0 40.0 0 2 0 359 #> 360 21 120.0 0 NA 0 1.9 0 1 0 360 #> 361 21 120.0 0 NA 0 44.0 0 2 0 361 #> 362 22 0.0 1 87.0 0 NA 1 0 0 362 #> 363 22 0.0 0 NA 0 100.0 0 2 0 363 #> 364 22 24.0 0 NA 0 8.9 0 1 0 364 #> 365 22 24.0 0 NA 0 35.0 0 2 0 365 #> 366 22 36.0 0 NA 0 8.4 0 1 0 366 #> 367 22 36.0 0 NA 0 27.0 0 2 0 367 #> 368 22 48.0 0 NA 0 8.0 0 1 0 368 #> 369 22 48.0 0 NA 0 23.0 0 2 0 369 #> 370 22 72.0 0 NA 0 4.4 0 1 0 370 #> 371 22 72.0 0 NA 0 27.0 0 2 0 371 #> 372 22 96.0 0 NA 0 3.2 0 1 0 372 #> 373 22 96.0 0 NA 0 43.0 0 2 0 373 #> 374 22 120.0 0 NA 0 1.7 0 1 0 374 #> 375 22 120.0 0 NA 0 43.0 0 2 0 375 #> 376 23 0.0 1 110.0 0 NA 1 0 0 376 #> 377 23 0.0 0 NA 0 100.0 0 2 0 377 #> 378 23 24.0 0 NA 0 9.8 0 1 0 378 #> 379 23 24.0 0 NA 0 34.0 0 2 0 379 #> 380 23 36.0 0 NA 0 8.4 0 1 0 380 #> 381 23 36.0 0 NA 0 24.0 0 2 0 381 #> 382 23 48.0 0 NA 0 6.6 0 1 0 382 #> 383 23 48.0 0 NA 0 15.0 0 2 0 383 #> 384 23 72.0 0 NA 0 4.8 0 1 0 384 #> 385 23 72.0 0 NA 0 15.0 0 2 0 385 #> 386 23 96.0 0 NA 0 3.2 0 1 0 386 #> 387 23 96.0 0 NA 0 19.0 0 2 0 387 #> 388 23 120.0 0 NA 0 2.4 0 1 0 388 #> 389 23 120.0 0 NA 0 19.0 0 2 0 389 #> 390 24 0.0 1 115.0 0 NA 1 0 0 390 #> 391 24 0.0 0 NA 0 88.0 0 2 0 391 #> 392 24 24.0 0 NA 0 8.2 0 1 0 392 #> 393 24 24.0 0 NA 0 37.0 0 2 0 393 #> 394 24 36.0 0 NA 0 7.5 0 1 0 394 #> 395 24 36.0 0 NA 0 20.0 0 2 0 395 #> 396 24 48.0 0 NA 0 6.8 0 1 0 396 #> 397 24 48.0 0 NA 0 20.0 0 2 0 397 #> 398 24 72.0 0 NA 0 5.5 0 1 0 398 #> 399 24 72.0 0 NA 0 26.0 0 2 0 399 #> 400 24 96.0 0 NA 0 4.5 0 1 0 400 #> 401 24 96.0 0 NA 0 28.0 0 2 0 401 #> 402 24 120.0 0 NA 0 3.7 0 1 0 402 #> 403 24 120.0 0 NA 0 50.0 0 2 0 403 #> 404 25 0.0 1 112.0 0 NA 1 0 0 404 #> 405 25 0.0 0 NA 0 100.0 0 2 0 405 #> 406 25 24.0 0 NA 0 11.0 0 1 0 406 #> 407 25 24.0 0 NA 0 32.0 0 2 0 407 #> 408 25 36.0 0 NA 0 10.0 0 1 0 408 #> 409 25 36.0 0 NA 0 20.0 0 2 0 409 #> 410 25 48.0 0 NA 0 8.2 0 1 0 410 #> 411 25 48.0 0 NA 0 17.0 0 2 0 411 #> 412 25 72.0 0 NA 0 6.0 0 1 0 412 #> 413 25 72.0 0 NA 0 19.0 0 2 0 413 #> 414 25 96.0 0 NA 0 3.7 0 1 0 414 #> 415 25 96.0 0 NA 0 21.0 0 2 0 415 #> 416 25 120.0 0 NA 0 2.6 0 1 0 416 #> 417 25 120.0 0 NA 0 30.0 0 2 0 417 #> 418 26 0.0 1 120.0 0 NA 1 0 0 418 #> 419 26 0.0 0 NA 0 100.0 0 2 0 419 #> 420 26 24.0 0 NA 0 10.0 0 1 0 420 #> 421 26 24.0 0 NA 0 41.0 0 2 0 421 #> 422 26 36.0 0 NA 0 9.0 0 1 0 422 #> 423 26 36.0 0 NA 0 28.0 0 2 0 423 #> 424 26 48.0 0 NA 0 7.3 0 1 0 424 #> 425 26 48.0 0 NA 0 19.0 0 2 0 425 #> 426 26 72.0 0 NA 0 5.2 0 1 0 426 #> 427 26 72.0 0 NA 0 17.0 0 2 0 427 #> 428 26 96.0 0 NA 0 3.7 0 1 0 428 #> 429 26 96.0 0 NA 0 17.0 0 2 0 429 #> 430 26 120.0 0 NA 0 2.7 0 1 0 430 #> 431 26 120.0 0 NA 0 24.0 0 2 0 431 #> 432 27 0.0 1 120.0 0 NA 1 0 0 432 #> 433 27 0.0 0 NA 0 100.0 0 2 0 433 #> 434 27 24.0 0 NA 0 11.8 0 1 0 434 #> 435 27 24.0 0 NA 0 32.0 0 2 0 435 #> 436 27 36.0 0 NA 0 9.2 0 1 0 436 #> 437 27 36.0 0 NA 0 21.0 0 2 0 437 #> 438 27 48.0 0 NA 0 7.7 0 1 0 438 #> 439 27 48.0 0 NA 0 19.0 0 2 0 439 #> 440 27 72.0 0 NA 0 4.9 0 1 0 440 #> 441 27 72.0 0 NA 0 22.0 0 2 0 441 #> 442 27 96.0 0 NA 0 3.4 0 1 0 442 #> 443 27 96.0 0 NA 0 33.0 0 2 0 443 #> 444 27 120.0 0 NA 0 2.7 0 1 0 444 #> 445 27 120.0 0 NA 0 46.0 0 2 0 445 #> 446 28 0.0 1 120.0 0 NA 1 0 0 446 #> 447 28 0.0 0 NA 0 100.0 0 2 0 447 #> 448 28 24.0 0 NA 0 10.1 0 1 0 448 #> 449 28 24.0 0 NA 0 39.0 0 2 0 449 #> 450 28 36.0 0 NA 0 8.0 0 1 0 450 #> 451 28 36.0 0 NA 0 25.0 0 2 0 451 #> 452 28 48.0 0 NA 0 6.0 0 1 0 452 #> 453 28 48.0 0 NA 0 16.0 0 2 0 453 #> 454 28 72.0 0 NA 0 4.9 0 1 0 454 #> 455 28 72.0 0 NA 0 14.0 0 2 0 455 #> 456 28 96.0 0 NA 0 3.4 0 1 0 456 #> 457 28 96.0 0 NA 0 15.0 0 2 0 457 #> 458 28 120.0 0 NA 0 2.0 0 1 0 458 #> 459 28 120.0 0 NA 0 20.0 0 2 0 459 #> 460 29 0.0 1 153.0 0 NA 1 0 0 460 #> 461 29 0.0 0 NA 0 86.0 0 2 0 461 #> 462 29 24.0 0 NA 0 8.3 0 1 0 462 #> 463 29 24.0 0 NA 0 35.0 0 2 0 463 #> 464 29 36.0 0 NA 0 7.0 0 1 0 464 #> 465 29 36.0 0 NA 0 21.0 0 2 0 465 #> 466 29 48.0 0 NA 0 5.6 0 1 0 466 #> 467 29 48.0 0 NA 0 18.0 0 2 0 467 #> 468 29 72.0 0 NA 0 4.1 0 1 0 468 #> 469 29 72.0 0 NA 0 20.0 0 2 0 469 #> 470 29 96.0 0 NA 0 3.1 0 1 0 470 #> 471 29 96.0 0 NA 0 29.0 0 2 0 471 #> 472 29 120.0 0 NA 0 2.2 0 1 0 472 #> 473 29 120.0 0 NA 0 41.0 0 2 0 473 #> 474 30 0.0 1 105.0 0 NA 1 0 0 474 #> 475 30 0.0 0 NA 0 100.0 0 2 0 475 #> 476 30 24.0 0 NA 0 9.9 0 1 0 476 #> 477 30 24.0 0 NA 0 45.0 0 2 0 477 #> 478 30 36.0 0 NA 0 7.5 0 1 0 478 #> 479 30 36.0 0 NA 0 24.0 0 2 0 479 #> 480 30 48.0 0 NA 0 6.5 0 1 0 480 #> 481 30 48.0 0 NA 0 23.0 0 2 0 481 #> 482 30 72.0 0 NA 0 4.1 0 1 0 482 #> 483 30 72.0 0 NA 0 26.0 0 2 0 483 #> 484 30 96.0 0 NA 0 2.9 0 1 0 484 #> 485 30 96.0 0 NA 0 28.0 0 2 0 485 #> 486 30 120.0 0 NA 0 2.3 0 1 0 486 #> 487 30 120.0 0 NA 0 39.0 0 2 0 487 #> 488 31 0.0 1 125.0 0 NA 1 0 0 488 #> 489 31 0.0 0 NA 0 100.0 0 2 0 489 #> 490 31 24.0 0 NA 0 9.5 0 1 0 490 #> 491 31 24.0 0 NA 0 45.0 0 2 0 491 #> 492 31 36.0 0 NA 0 7.8 0 1 0 492 #> 493 31 36.0 0 NA 0 30.0 0 2 0 493 #> 494 31 48.0 0 NA 0 6.4 0 1 0 494 #> 495 31 48.0 0 NA 0 24.0 0 2 0 495 #> 496 31 72.0 0 NA 0 4.5 0 1 0 496 #> 497 31 72.0 0 NA 0 22.0 0 2 0 497 #> 498 31 96.0 0 NA 0 3.4 0 1 0 498 #> 499 31 96.0 0 NA 0 28.0 0 2 0 499 #> 500 31 120.0 0 NA 0 2.5 0 1 0 500 #> 501 31 120.0 0 NA 0 42.0 0 2 0 501 #> 502 32 0.0 1 93.0 0 NA 1 0 0 502 #> 503 32 0.0 0 NA 0 100.0 0 2 0 503 #> 504 32 24.0 0 NA 0 8.9 0 1 0 504 #> 505 32 24.0 0 NA 0 36.0 0 2 0 505 #> 506 32 36.0 0 NA 0 7.7 0 1 0 506 #> 507 32 36.0 0 NA 0 27.0 0 2 0 507 #> 508 32 48.0 0 NA 0 6.9 0 1 0 508 #> 509 32 48.0 0 NA 0 24.0 0 2 0 509 #> 510 32 72.0 0 NA 0 4.4 0 1 0 510 #> 511 32 72.0 0 NA 0 23.0 0 2 0 511 #> 512 32 96.0 0 NA 0 3.5 0 1 0 512 #> 513 32 96.0 0 NA 0 20.0 0 2 0 513 #> 514 32 120.0 0 NA 0 2.5 0 1 0 514 #> 515 32 120.0 0 NA 0 22.0 0 2 0 515 #> #> $adm #> adm cmt type #> 1 1 1 bolus #> bblDatToNonmem(pk.turnover.emax3, nlmixr2data::warfarin) #> #> #> ID TIME EVID AMT DV CMT DVID nlmixrRowNums #> 1 1 0.0 1 100.0 NA 1 NA 1 #> 2 1 0.5 0 NA 0.0 NA 1 2 #> 3 1 1.0 0 NA 1.9 NA 1 3 #> 4 1 2.0 0 NA 3.3 NA 1 4 #> 5 1 3.0 0 NA 6.6 NA 1 5 #> 6 1 6.0 0 NA 9.1 NA 1 6 #> 7 1 9.0 0 NA 10.8 NA 1 7 #> 8 1 12.0 0 NA 8.6 NA 1 8 #> 9 1 24.0 0 NA 5.6 NA 1 9 #> 10 1 24.0 0 NA 44.0 NA 2 10 #> 11 1 36.0 0 NA 4.0 NA 1 11 #> 12 1 36.0 0 NA 27.0 NA 2 12 #> 13 1 48.0 0 NA 2.7 NA 1 13 #> 14 1 48.0 0 NA 28.0 NA 2 14 #> 15 1 72.0 0 NA 0.8 NA 1 15 #> 16 1 72.0 0 NA 31.0 NA 2 16 #> 17 1 96.0 0 NA 60.0 NA 2 17 #> 18 1 120.0 0 NA 65.0 NA 2 18 #> 19 1 144.0 0 NA 71.0 NA 2 19 #> 20 2 0.0 1 100.0 NA 1 NA 20 #> 21 2 0.0 0 NA 100.0 NA 2 21 #> 22 2 24.0 0 NA 9.2 NA 1 22 #> 23 2 24.0 0 NA 49.0 NA 2 23 #> 24 2 36.0 0 NA 8.5 NA 1 24 #> 25 2 36.0 0 NA 32.0 NA 2 25 #> 26 2 48.0 0 NA 6.4 NA 1 26 #> 27 2 48.0 0 NA 26.0 NA 2 27 #> 28 2 72.0 0 NA 4.8 NA 1 28 #> 29 2 72.0 0 NA 22.0 NA 2 29 #> 30 2 96.0 0 NA 3.1 NA 1 30 #> 31 2 96.0 0 NA 28.0 NA 2 31 #> 32 2 120.0 0 NA 2.5 NA 1 32 #> 33 2 120.0 0 NA 33.0 NA 2 33 #> 34 3 0.0 1 100.0 NA 1 NA 34 #> 35 3 0.0 0 NA 100.0 NA 2 35 #> 36 3 0.5 0 NA 0.0 NA 1 36 #> 37 3 2.0 0 NA 8.4 NA 1 37 #> 38 3 3.0 0 NA 9.7 NA 1 38 #> 39 3 6.0 0 NA 9.8 NA 1 39 #> 40 3 12.0 0 NA 11.0 NA 1 40 #> 41 3 24.0 0 NA 8.3 NA 1 41 #> 42 3 24.0 0 NA 46.0 NA 2 42 #> 43 3 36.0 0 NA 7.7 NA 1 43 #> 44 3 36.0 0 NA 22.0 NA 2 44 #> 45 3 48.0 0 NA 6.3 NA 1 45 #> 46 3 48.0 0 NA 19.0 NA 2 46 #> 47 3 72.0 0 NA 4.1 NA 1 47 #> 48 3 72.0 0 NA 20.0 NA 2 48 #> 49 3 96.0 0 NA 3.0 NA 1 49 #> 50 3 96.0 0 NA 42.0 NA 2 50 #> 51 3 120.0 0 NA 1.4 NA 1 51 #> 52 3 120.0 0 NA 49.0 NA 2 52 #> 53 3 144.0 0 NA 54.0 NA 2 53 #> 54 4 0.0 1 120.0 NA 1 NA 54 #> 55 4 0.0 0 NA 100.0 NA 2 55 #> 56 4 3.0 0 NA 12.0 NA 1 56 #> 57 4 6.0 0 NA 13.2 NA 1 57 #> 58 4 9.0 0 NA 14.4 NA 1 58 #> 59 4 24.0 0 NA 9.6 NA 1 59 #> 60 4 24.0 0 NA 30.0 NA 2 60 #> 61 4 36.0 0 NA 8.2 NA 1 61 #> 62 4 36.0 0 NA 24.0 NA 2 62 #> 63 4 48.0 0 NA 7.8 NA 1 63 #> 64 4 48.0 0 NA 13.0 NA 2 64 #> 65 4 72.0 0 NA 5.8 NA 1 65 #> 66 4 72.0 0 NA 9.0 NA 2 66 #> 67 4 96.0 0 NA 4.3 NA 1 67 #> 68 4 96.0 0 NA 9.0 NA 2 68 #> 69 4 120.0 0 NA 3.0 NA 1 69 #> 70 4 120.0 0 NA 11.0 NA 2 70 #> 71 4 144.0 0 NA 12.0 NA 2 71 #> 72 5 0.0 1 60.0 NA 1 NA 72 #> 73 5 0.0 0 NA 82.0 NA 2 73 #> 74 5 3.0 0 NA 11.1 NA 1 74 #> 75 5 6.0 0 NA 11.9 NA 1 75 #> 76 5 9.0 0 NA 9.8 NA 1 76 #> 77 5 12.0 0 NA 11.0 NA 1 77 #> 78 5 24.0 0 NA 8.5 NA 1 78 #> 79 5 24.0 0 NA 43.0 NA 2 79 #> 80 5 36.0 0 NA 7.6 NA 1 80 #> 81 5 36.0 0 NA 25.0 NA 2 81 #> 82 5 48.0 0 NA 5.4 NA 1 82 #> 83 5 48.0 0 NA 18.0 NA 2 83 #> 84 5 72.0 0 NA 4.5 NA 1 84 #> 85 5 72.0 0 NA 17.0 NA 2 85 #> 86 5 96.0 0 NA 3.3 NA 1 86 #> 87 5 96.0 0 NA 23.0 NA 2 87 #> 88 5 120.0 0 NA 2.3 NA 1 88 #> 89 5 120.0 0 NA 29.0 NA 2 89 #> 90 5 144.0 0 NA 41.0 NA 2 90 #> 91 6 0.0 1 113.0 NA 1 NA 91 #> 92 6 0.0 0 NA 100.0 NA 2 92 #> 93 6 6.0 0 NA 8.6 NA 1 93 #> 94 6 12.0 0 NA 8.6 NA 1 94 #> 95 6 24.0 0 NA 7.0 NA 1 95 #> 96 6 24.0 0 NA 34.0 NA 2 96 #> 97 6 36.0 0 NA 5.7 NA 1 97 #> 98 6 36.0 0 NA 23.0 NA 2 98 #> 99 6 48.0 0 NA 4.7 NA 1 99 #> 100 6 48.0 0 NA 20.0 NA 2 100 #> 101 6 72.0 0 NA 3.3 NA 1 101 #> 102 6 72.0 0 NA 16.0 NA 2 102 #> 103 6 96.0 0 NA 2.3 NA 1 103 #> 104 6 96.0 0 NA 17.0 NA 2 104 #> 105 6 120.0 0 NA 1.7 NA 1 105 #> 106 6 120.0 0 NA 18.0 NA 2 106 #> 107 6 144.0 0 NA 25.0 NA 2 107 #> 108 7 0.0 1 90.0 NA 1 NA 108 #> 109 7 3.0 0 NA 13.4 NA 1 109 #> 110 7 6.0 0 NA 12.4 NA 1 110 #> 111 7 9.0 0 NA 12.7 NA 1 111 #> 112 7 12.0 0 NA 8.8 NA 1 112 #> 113 7 24.0 0 NA 6.1 NA 1 113 #> 114 7 24.0 0 NA 36.0 NA 2 114 #> 115 7 36.0 0 NA 3.5 NA 1 115 #> 116 7 36.0 0 NA 33.0 NA 2 116 #> 117 7 48.0 0 NA 1.8 NA 1 117 #> 118 7 48.0 0 NA 28.0 NA 2 118 #> 119 7 72.0 0 NA 1.5 NA 1 119 #> 120 7 72.0 0 NA 52.0 NA 2 120 #> 121 7 96.0 0 NA 1.0 NA 1 121 #> 122 7 96.0 0 NA 80.0 NA 2 122 #> 123 7 120.0 0 NA 90.0 NA 2 123 #> 124 7 144.0 0 NA 100.0 NA 2 124 #> 125 8 0.0 1 135.0 NA 1 NA 125 #> 126 8 0.0 0 NA 88.0 NA 2 126 #> 127 8 2.0 0 NA 17.6 NA 1 127 #> 128 8 3.0 0 NA 17.3 NA 1 128 #> 129 8 6.0 0 NA 15.0 NA 1 129 #> 130 8 9.0 0 NA 15.0 NA 1 130 #> 131 8 12.0 0 NA 12.4 NA 1 131 #> 132 8 24.0 0 NA 7.9 NA 1 132 #> 133 8 24.0 0 NA 35.0 NA 2 133 #> 134 8 36.0 0 NA 7.9 NA 1 134 #> 135 8 36.0 0 NA 20.0 NA 2 135 #> 136 8 48.0 0 NA 5.1 NA 1 136 #> 137 8 48.0 0 NA 12.0 NA 2 137 #> 138 8 72.0 0 NA 3.6 NA 1 138 #> 139 8 72.0 0 NA 16.0 NA 2 139 #> 140 8 96.0 0 NA 2.4 NA 1 140 #> 141 8 96.0 0 NA 23.0 NA 2 141 #> 142 8 120.0 0 NA 2.0 NA 1 142 #> 143 8 120.0 0 NA 36.0 NA 2 143 #> 144 8 144.0 0 NA 48.0 NA 2 144 #> 145 9 0.0 1 75.0 NA 1 NA 145 #> 146 9 0.0 0 NA 92.0 NA 2 146 #> 147 9 0.5 0 NA 0.0 NA 1 147 #> 148 9 1.0 0 NA 1.0 NA 1 148 #> 149 9 2.0 0 NA 4.6 NA 1 149 #> 150 9 3.0 0 NA 12.7 NA 1 150 #> 151 9 3.0 0 NA 8.0 NA 1 151 #> 152 9 6.0 0 NA 12.7 NA 1 152 #> 153 9 6.0 0 NA 11.5 NA 1 153 #> 154 9 9.0 0 NA 12.9 NA 1 154 #> 155 9 9.0 0 NA 11.4 NA 1 155 #> 156 9 12.0 0 NA 11.4 NA 1 156 #> 157 9 12.0 0 NA 11.0 NA 1 157 #> 158 9 24.0 0 NA 9.1 NA 1 158 #> 159 9 24.0 0 NA 33.0 NA 2 159 #> 160 9 36.0 0 NA 8.2 NA 1 160 #> 161 9 36.0 0 NA 22.0 NA 2 161 #> 162 9 48.0 0 NA 5.9 NA 1 162 #> 163 9 48.0 0 NA 16.0 NA 2 163 #> 164 9 72.0 0 NA 3.6 NA 1 164 #> 165 9 72.0 0 NA 18.0 NA 2 165 #> 166 9 96.0 0 NA 1.7 NA 1 166 #> 167 9 96.0 0 NA 32.0 NA 2 167 #> 168 9 120.0 0 NA 1.1 NA 1 168 #> 169 9 120.0 0 NA 30.0 NA 2 169 #> 170 9 144.0 0 NA 45.0 NA 2 170 #> 171 10 0.0 1 105.0 NA 1 NA 171 #> 172 10 0.0 0 NA 90.0 NA 2 172 #> 173 10 24.0 0 NA 8.6 NA 1 173 #> 174 10 24.0 0 NA 39.0 NA 2 174 #> 175 10 36.0 0 NA 8.0 NA 1 175 #> 176 10 36.0 0 NA 22.0 NA 2 176 #> 177 10 48.0 0 NA 6.0 NA 1 177 #> 178 10 48.0 0 NA 17.0 NA 2 178 #> 179 10 72.0 0 NA 4.4 NA 1 179 #> 180 10 72.0 0 NA 17.0 NA 2 180 #> 181 10 96.0 0 NA 3.6 NA 1 181 #> 182 10 96.0 0 NA 22.0 NA 2 182 #> 183 10 120.0 0 NA 2.8 NA 1 183 #> 184 10 120.0 0 NA 25.0 NA 2 184 #> 185 10 144.0 0 NA 33.0 NA 2 185 #> 186 11 0.0 1 123.0 NA 1 NA 186 #> 187 11 0.0 0 NA 100.0 NA 2 187 #> 188 11 1.5 0 NA 11.4 NA 1 188 #> 189 11 3.0 0 NA 15.4 NA 1 189 #> 190 11 6.0 0 NA 17.5 NA 1 190 #> 191 11 12.0 0 NA 14.0 NA 1 191 #> 192 11 24.0 0 NA 9.0 NA 1 192 #> 193 11 24.0 0 NA 37.0 NA 2 193 #> 194 11 36.0 0 NA 8.9 NA 1 194 #> 195 11 36.0 0 NA 24.0 NA 2 195 #> 196 11 48.0 0 NA 6.6 NA 1 196 #> 197 11 48.0 0 NA 14.0 NA 2 197 #> 198 11 72.0 0 NA 4.2 NA 1 198 #> 199 11 72.0 0 NA 11.0 NA 2 199 #> 200 11 96.0 0 NA 3.6 NA 1 200 #> 201 11 96.0 0 NA 14.0 NA 2 201 #> 202 11 120.0 0 NA 2.6 NA 1 202 #> 203 11 120.0 0 NA 23.0 NA 2 203 #> 204 11 144.0 0 NA 33.0 NA 2 204 #> 205 12 0.0 1 113.0 NA 1 NA 205 #> 206 12 0.0 0 NA 85.0 NA 2 206 #> 207 12 1.5 0 NA 0.6 NA 1 207 #> 208 12 3.0 0 NA 2.8 NA 1 208 #> 209 12 6.0 0 NA 13.8 NA 1 209 #> 210 12 9.0 0 NA 15.0 NA 1 210 #> 211 12 24.0 0 NA 10.5 NA 1 211 #> 212 12 24.0 0 NA 25.0 NA 2 212 #> 213 12 36.0 0 NA 9.1 NA 1 213 #> 214 12 36.0 0 NA 15.0 NA 2 214 #> 215 12 48.0 0 NA 6.6 NA 1 215 #> 216 12 48.0 0 NA 11.0 NA 2 216 #> 217 12 72.0 0 NA 4.9 NA 1 217 #> 218 12 96.0 0 NA 2.4 NA 1 218 #> 219 12 120.0 0 NA 1.9 NA 1 219 #> 220 13 0.0 1 113.0 NA 1 NA 220 #> 221 13 0.0 0 NA 88.0 NA 2 221 #> 222 13 1.5 0 NA 3.6 NA 1 222 #> 223 13 3.0 0 NA 12.9 NA 1 223 #> 224 13 6.0 0 NA 12.9 NA 1 224 #> 225 13 9.0 0 NA 10.2 NA 1 225 #> 226 13 24.0 0 NA 6.4 NA 1 226 #> 227 13 24.0 0 NA 41.0 NA 2 227 #> 228 13 36.0 0 NA 6.9 NA 1 228 #> 229 13 36.0 0 NA 23.0 NA 2 229 #> 230 13 48.0 0 NA 4.5 NA 1 230 #> 231 13 48.0 0 NA 16.0 NA 2 231 #> 232 13 72.0 0 NA 3.2 NA 1 232 #> 233 13 72.0 0 NA 14.0 NA 2 233 #> 234 13 96.0 0 NA 2.4 NA 1 234 #> 235 13 96.0 0 NA 18.0 NA 2 235 #> 236 13 120.0 0 NA 1.3 NA 1 236 #> 237 13 120.0 0 NA 22.0 NA 2 237 #> 238 13 144.0 0 NA 35.0 NA 2 238 #> 239 14 0.0 1 75.0 NA 1 NA 239 #> 240 14 0.0 0 NA 85.0 NA 2 240 #> 241 14 0.5 0 NA 0.0 NA 1 241 #> 242 14 1.0 0 NA 2.7 NA 1 242 #> 243 14 2.0 0 NA 11.6 NA 1 243 #> 244 14 3.0 0 NA 11.6 NA 1 244 #> 245 14 6.0 0 NA 11.3 NA 1 245 #> 246 14 9.0 0 NA 9.7 NA 1 246 #> 247 14 24.0 0 NA 6.5 NA 1 247 #> 248 14 24.0 0 NA 32.0 NA 2 248 #> 249 14 36.0 0 NA 5.2 NA 1 249 #> 250 14 36.0 0 NA 22.0 NA 2 250 #> 251 14 48.0 0 NA 3.6 NA 1 251 #> 252 14 48.0 0 NA 21.0 NA 2 252 #> 253 14 72.0 0 NA 2.4 NA 1 253 #> 254 14 72.0 0 NA 28.0 NA 2 254 #> 255 14 96.0 0 NA 0.9 NA 1 255 #> 256 14 96.0 0 NA 38.0 NA 2 256 #> 257 14 120.0 0 NA 46.0 NA 2 257 #> 258 14 144.0 0 NA 65.0 NA 2 258 #> 259 15 0.0 1 85.0 NA 1 NA 259 #> 260 15 0.0 0 NA 100.0 NA 2 260 #> 261 15 1.0 0 NA 6.6 NA 1 261 #> 262 15 3.0 0 NA 11.9 NA 1 262 #> 263 15 6.0 0 NA 11.7 NA 1 263 #> 264 15 9.0 0 NA 12.2 NA 1 264 #> 265 15 24.0 0 NA 8.1 NA 1 265 #> 266 15 24.0 0 NA 43.0 NA 2 266 #> 267 15 36.0 0 NA 7.4 NA 1 267 #> 268 15 36.0 0 NA 26.0 NA 2 268 #> 269 15 48.0 0 NA 6.8 NA 1 269 #> 270 15 48.0 0 NA 15.0 NA 2 270 #> 271 15 72.0 0 NA 5.3 NA 1 271 #> 272 15 72.0 0 NA 13.0 NA 2 272 #> 273 15 96.0 0 NA 3.0 NA 1 273 #> 274 15 96.0 0 NA 21.0 NA 2 274 #> 275 15 120.0 0 NA 2.0 NA 1 275 #> 276 15 120.0 0 NA 28.0 NA 2 276 #> 277 15 144.0 0 NA 39.0 NA 2 277 #> 278 16 0.0 1 87.0 NA 1 NA 278 #> 279 16 0.0 0 NA 100.0 NA 2 279 #> 280 16 24.0 0 NA 10.4 NA 1 280 #> 281 16 24.0 0 NA 42.0 NA 2 281 #> 282 16 36.0 0 NA 8.9 NA 1 282 #> 283 16 36.0 0 NA 32.0 NA 2 283 #> 284 16 48.0 0 NA 7.0 NA 1 284 #> 285 16 48.0 0 NA 26.0 NA 2 285 #> 286 16 72.0 0 NA 4.4 NA 1 286 #> 287 16 72.0 0 NA 31.0 NA 2 287 #> 288 16 96.0 0 NA 3.2 NA 1 288 #> 289 16 96.0 0 NA 33.0 NA 2 289 #> 290 16 120.0 0 NA 2.4 NA 1 290 #> 291 16 120.0 0 NA 54.0 NA 2 291 #> 292 17 0.0 1 117.0 NA 1 NA 292 #> 293 17 0.0 0 NA 100.0 NA 2 293 #> 294 17 24.0 0 NA 7.6 NA 1 294 #> 295 17 24.0 0 NA 35.0 NA 2 295 #> 296 17 36.0 0 NA 6.4 NA 1 296 #> 297 17 36.0 0 NA 23.0 NA 2 297 #> 298 17 48.0 0 NA 6.0 NA 1 298 #> 299 17 48.0 0 NA 17.0 NA 2 299 #> 300 17 72.0 0 NA 4.0 NA 1 300 #> 301 17 72.0 0 NA 18.0 NA 2 301 #> 302 17 96.0 0 NA 3.1 NA 1 302 #> 303 17 96.0 0 NA 18.0 NA 2 303 #> 304 17 120.0 0 NA 2.0 NA 1 304 #> 305 17 120.0 0 NA 21.0 NA 2 305 #> 306 18 0.0 1 112.0 NA 1 NA 306 #> 307 18 0.0 0 NA 100.0 NA 2 307 #> 308 18 24.0 0 NA 7.6 NA 1 308 #> 309 18 24.0 0 NA 32.0 NA 2 309 #> 310 18 36.0 0 NA 6.6 NA 1 310 #> 311 18 36.0 0 NA 20.0 NA 2 311 #> 312 18 48.0 0 NA 5.4 NA 1 312 #> 313 18 48.0 0 NA 18.0 NA 2 313 #> 314 18 72.0 0 NA 3.4 NA 1 314 #> 315 18 72.0 0 NA 18.0 NA 2 315 #> 316 18 96.0 0 NA 1.2 NA 1 316 #> 317 18 96.0 0 NA 19.0 NA 2 317 #> 318 18 120.0 0 NA 0.9 NA 1 318 #> 319 18 120.0 0 NA 29.0 NA 2 319 #> 320 19 0.0 1 95.5 NA 1 NA 320 #> 321 19 0.0 0 NA 100.0 NA 2 321 #> 322 19 24.0 0 NA 6.6 NA 1 322 #> 323 19 24.0 0 NA 33.0 NA 2 323 #> 324 19 36.0 0 NA 5.3 NA 1 324 #> 325 19 36.0 0 NA 28.0 NA 2 325 #> 326 19 48.0 0 NA 3.6 NA 1 326 #> 327 19 48.0 0 NA 18.0 NA 2 327 #> 328 19 72.0 0 NA 2.7 NA 1 328 #> 329 19 72.0 0 NA 18.0 NA 2 329 #> 330 19 96.0 0 NA 1.4 NA 1 330 #> 331 19 96.0 0 NA 17.0 NA 2 331 #> 332 19 120.0 0 NA 1.1 NA 1 332 #> 333 19 120.0 0 NA 26.0 NA 2 333 #> 334 20 0.0 1 88.5 NA 1 NA 334 #> 335 20 0.0 0 NA 100.0 NA 2 335 #> 336 20 24.0 0 NA 9.6 NA 1 336 #> 337 20 24.0 0 NA 41.0 NA 2 337 #> 338 20 36.0 0 NA 8.0 NA 1 338 #> 339 20 36.0 0 NA 30.0 NA 2 339 #> 340 20 48.0 0 NA 6.6 NA 1 340 #> 341 20 48.0 0 NA 22.0 NA 2 341 #> 342 20 72.0 0 NA 5.6 NA 1 342 #> 343 20 72.0 0 NA 23.0 NA 2 343 #> 344 20 96.0 0 NA 3.5 NA 1 344 #> 345 20 96.0 0 NA 23.0 NA 2 345 #> 346 20 120.0 0 NA 2.3 NA 1 346 #> 347 20 120.0 0 NA 35.0 NA 2 347 #> 348 21 0.0 1 93.0 NA 1 NA 348 #> 349 21 0.0 0 NA 100.0 NA 2 349 #> 350 21 24.0 0 NA 7.3 NA 1 350 #> 351 21 24.0 0 NA 46.0 NA 2 351 #> 352 21 36.0 0 NA 6.1 NA 1 352 #> 353 21 36.0 0 NA 27.0 NA 2 353 #> 354 21 48.0 0 NA 4.3 NA 1 354 #> 355 21 48.0 0 NA 22.0 NA 2 355 #> 356 21 72.0 0 NA 3.2 NA 1 356 #> 357 21 72.0 0 NA 36.0 NA 2 357 #> 358 21 96.0 0 NA 2.3 NA 1 358 #> 359 21 96.0 0 NA 40.0 NA 2 359 #> 360 21 120.0 0 NA 1.9 NA 1 360 #> 361 21 120.0 0 NA 44.0 NA 2 361 #> 362 22 0.0 1 87.0 NA 1 NA 362 #> 363 22 0.0 0 NA 100.0 NA 2 363 #> 364 22 24.0 0 NA 8.9 NA 1 364 #> 365 22 24.0 0 NA 35.0 NA 2 365 #> 366 22 36.0 0 NA 8.4 NA 1 366 #> 367 22 36.0 0 NA 27.0 NA 2 367 #> 368 22 48.0 0 NA 8.0 NA 1 368 #> 369 22 48.0 0 NA 23.0 NA 2 369 #> 370 22 72.0 0 NA 4.4 NA 1 370 #> 371 22 72.0 0 NA 27.0 NA 2 371 #> 372 22 96.0 0 NA 3.2 NA 1 372 #> 373 22 96.0 0 NA 43.0 NA 2 373 #> 374 22 120.0 0 NA 1.7 NA 1 374 #> 375 22 120.0 0 NA 43.0 NA 2 375 #> 376 23 0.0 1 110.0 NA 1 NA 376 #> 377 23 0.0 0 NA 100.0 NA 2 377 #> 378 23 24.0 0 NA 9.8 NA 1 378 #> 379 23 24.0 0 NA 34.0 NA 2 379 #> 380 23 36.0 0 NA 8.4 NA 1 380 #> 381 23 36.0 0 NA 24.0 NA 2 381 #> 382 23 48.0 0 NA 6.6 NA 1 382 #> 383 23 48.0 0 NA 15.0 NA 2 383 #> 384 23 72.0 0 NA 4.8 NA 1 384 #> 385 23 72.0 0 NA 15.0 NA 2 385 #> 386 23 96.0 0 NA 3.2 NA 1 386 #> 387 23 96.0 0 NA 19.0 NA 2 387 #> 388 23 120.0 0 NA 2.4 NA 1 388 #> 389 23 120.0 0 NA 19.0 NA 2 389 #> 390 24 0.0 1 115.0 NA 1 NA 390 #> 391 24 0.0 0 NA 88.0 NA 2 391 #> 392 24 24.0 0 NA 8.2 NA 1 392 #> 393 24 24.0 0 NA 37.0 NA 2 393 #> 394 24 36.0 0 NA 7.5 NA 1 394 #> 395 24 36.0 0 NA 20.0 NA 2 395 #> 396 24 48.0 0 NA 6.8 NA 1 396 #> 397 24 48.0 0 NA 20.0 NA 2 397 #> 398 24 72.0 0 NA 5.5 NA 1 398 #> 399 24 72.0 0 NA 26.0 NA 2 399 #> 400 24 96.0 0 NA 4.5 NA 1 400 #> 401 24 96.0 0 NA 28.0 NA 2 401 #> 402 24 120.0 0 NA 3.7 NA 1 402 #> 403 24 120.0 0 NA 50.0 NA 2 403 #> 404 25 0.0 1 112.0 NA 1 NA 404 #> 405 25 0.0 0 NA 100.0 NA 2 405 #> 406 25 24.0 0 NA 11.0 NA 1 406 #> 407 25 24.0 0 NA 32.0 NA 2 407 #> 408 25 36.0 0 NA 10.0 NA 1 408 #> 409 25 36.0 0 NA 20.0 NA 2 409 #> 410 25 48.0 0 NA 8.2 NA 1 410 #> 411 25 48.0 0 NA 17.0 NA 2 411 #> 412 25 72.0 0 NA 6.0 NA 1 412 #> 413 25 72.0 0 NA 19.0 NA 2 413 #> 414 25 96.0 0 NA 3.7 NA 1 414 #> 415 25 96.0 0 NA 21.0 NA 2 415 #> 416 25 120.0 0 NA 2.6 NA 1 416 #> 417 25 120.0 0 NA 30.0 NA 2 417 #> 418 26 0.0 1 120.0 NA 1 NA 418 #> 419 26 0.0 0 NA 100.0 NA 2 419 #> 420 26 24.0 0 NA 10.0 NA 1 420 #> 421 26 24.0 0 NA 41.0 NA 2 421 #> 422 26 36.0 0 NA 9.0 NA 1 422 #> 423 26 36.0 0 NA 28.0 NA 2 423 #> 424 26 48.0 0 NA 7.3 NA 1 424 #> 425 26 48.0 0 NA 19.0 NA 2 425 #> 426 26 72.0 0 NA 5.2 NA 1 426 #> 427 26 72.0 0 NA 17.0 NA 2 427 #> 428 26 96.0 0 NA 3.7 NA 1 428 #> 429 26 96.0 0 NA 17.0 NA 2 429 #> 430 26 120.0 0 NA 2.7 NA 1 430 #> 431 26 120.0 0 NA 24.0 NA 2 431 #> 432 27 0.0 1 120.0 NA 1 NA 432 #> 433 27 0.0 0 NA 100.0 NA 2 433 #> 434 27 24.0 0 NA 11.8 NA 1 434 #> 435 27 24.0 0 NA 32.0 NA 2 435 #> 436 27 36.0 0 NA 9.2 NA 1 436 #> 437 27 36.0 0 NA 21.0 NA 2 437 #> 438 27 48.0 0 NA 7.7 NA 1 438 #> 439 27 48.0 0 NA 19.0 NA 2 439 #> 440 27 72.0 0 NA 4.9 NA 1 440 #> 441 27 72.0 0 NA 22.0 NA 2 441 #> 442 27 96.0 0 NA 3.4 NA 1 442 #> 443 27 96.0 0 NA 33.0 NA 2 443 #> 444 27 120.0 0 NA 2.7 NA 1 444 #> 445 27 120.0 0 NA 46.0 NA 2 445 #> 446 28 0.0 1 120.0 NA 1 NA 446 #> 447 28 0.0 0 NA 100.0 NA 2 447 #> 448 28 24.0 0 NA 10.1 NA 1 448 #> 449 28 24.0 0 NA 39.0 NA 2 449 #> 450 28 36.0 0 NA 8.0 NA 1 450 #> 451 28 36.0 0 NA 25.0 NA 2 451 #> 452 28 48.0 0 NA 6.0 NA 1 452 #> 453 28 48.0 0 NA 16.0 NA 2 453 #> 454 28 72.0 0 NA 4.9 NA 1 454 #> 455 28 72.0 0 NA 14.0 NA 2 455 #> 456 28 96.0 0 NA 3.4 NA 1 456 #> 457 28 96.0 0 NA 15.0 NA 2 457 #> 458 28 120.0 0 NA 2.0 NA 1 458 #> 459 28 120.0 0 NA 20.0 NA 2 459 #> 460 29 0.0 1 153.0 NA 1 NA 460 #> 461 29 0.0 0 NA 86.0 NA 2 461 #> 462 29 24.0 0 NA 8.3 NA 1 462 #> 463 29 24.0 0 NA 35.0 NA 2 463 #> 464 29 36.0 0 NA 7.0 NA 1 464 #> 465 29 36.0 0 NA 21.0 NA 2 465 #> 466 29 48.0 0 NA 5.6 NA 1 466 #> 467 29 48.0 0 NA 18.0 NA 2 467 #> 468 29 72.0 0 NA 4.1 NA 1 468 #> 469 29 72.0 0 NA 20.0 NA 2 469 #> 470 29 96.0 0 NA 3.1 NA 1 470 #> 471 29 96.0 0 NA 29.0 NA 2 471 #> 472 29 120.0 0 NA 2.2 NA 1 472 #> 473 29 120.0 0 NA 41.0 NA 2 473 #> 474 30 0.0 1 105.0 NA 1 NA 474 #> 475 30 0.0 0 NA 100.0 NA 2 475 #> 476 30 24.0 0 NA 9.9 NA 1 476 #> 477 30 24.0 0 NA 45.0 NA 2 477 #> 478 30 36.0 0 NA 7.5 NA 1 478 #> 479 30 36.0 0 NA 24.0 NA 2 479 #> 480 30 48.0 0 NA 6.5 NA 1 480 #> 481 30 48.0 0 NA 23.0 NA 2 481 #> 482 30 72.0 0 NA 4.1 NA 1 482 #> 483 30 72.0 0 NA 26.0 NA 2 483 #> 484 30 96.0 0 NA 2.9 NA 1 484 #> 485 30 96.0 0 NA 28.0 NA 2 485 #> 486 30 120.0 0 NA 2.3 NA 1 486 #> 487 30 120.0 0 NA 39.0 NA 2 487 #> 488 31 0.0 1 125.0 NA 1 NA 488 #> 489 31 0.0 0 NA 100.0 NA 2 489 #> 490 31 24.0 0 NA 9.5 NA 1 490 #> 491 31 24.0 0 NA 45.0 NA 2 491 #> 492 31 36.0 0 NA 7.8 NA 1 492 #> 493 31 36.0 0 NA 30.0 NA 2 493 #> 494 31 48.0 0 NA 6.4 NA 1 494 #> 495 31 48.0 0 NA 24.0 NA 2 495 #> 496 31 72.0 0 NA 4.5 NA 1 496 #> 497 31 72.0 0 NA 22.0 NA 2 497 #> 498 31 96.0 0 NA 3.4 NA 1 498 #> 499 31 96.0 0 NA 28.0 NA 2 499 #> 500 31 120.0 0 NA 2.5 NA 1 500 #> 501 31 120.0 0 NA 42.0 NA 2 501 #> 502 32 0.0 1 93.0 NA 1 NA 502 #> 503 32 0.0 0 NA 100.0 NA 2 503 #> 504 32 24.0 0 NA 8.9 NA 1 504 #> 505 32 24.0 0 NA 36.0 NA 2 505 #> 506 32 36.0 0 NA 7.7 NA 1 506 #> 507 32 36.0 0 NA 27.0 NA 2 507 #> 508 32 48.0 0 NA 6.9 NA 1 508 #> 509 32 48.0 0 NA 24.0 NA 2 509 #> 510 32 72.0 0 NA 4.4 NA 1 510 #> 511 32 72.0 0 NA 23.0 NA 2 511 #> 512 32 96.0 0 NA 3.5 NA 1 512 #> 513 32 96.0 0 NA 20.0 NA 2 513 #> 514 32 120.0 0 NA 2.5 NA 1 514 #> 515 32 120.0 0 NA 22.0 NA 2 515 bblDatToMrgsolve(pk.turnover.emax3, nlmixr2data::warfarin) #> #> #> ID TIME EVID AMT II DV CMT DVID SS nlmixrRowNums #> 1 1 0.0 1 100.0 0 NA 1 0 0 1 #> 2 1 0.5 0 NA 0 0.0 5 1 0 2 #> 3 1 1.0 0 NA 0 1.9 5 1 0 3 #> 4 1 2.0 0 NA 0 3.3 5 1 0 4 #> 5 1 3.0 0 NA 0 6.6 5 1 0 5 #> 6 1 6.0 0 NA 0 9.1 5 1 0 6 #> 7 1 9.0 0 NA 0 10.8 5 1 0 7 #> 8 1 12.0 0 NA 0 8.6 5 1 0 8 #> 9 1 24.0 0 NA 0 5.6 5 1 0 9 #> 10 1 24.0 0 NA 0 44.0 6 2 0 10 #> 11 1 36.0 0 NA 0 4.0 5 1 0 11 #> 12 1 36.0 0 NA 0 27.0 6 2 0 12 #> 13 1 48.0 0 NA 0 2.7 5 1 0 13 #> 14 1 48.0 0 NA 0 28.0 6 2 0 14 #> 15 1 72.0 0 NA 0 0.8 5 1 0 15 #> 16 1 72.0 0 NA 0 31.0 6 2 0 16 #> 17 1 96.0 0 NA 0 60.0 6 2 0 17 #> 18 1 120.0 0 NA 0 65.0 6 2 0 18 #> 19 1 144.0 0 NA 0 71.0 6 2 0 19 #> 20 2 0.0 1 100.0 0 NA 1 0 0 20 #> 21 2 0.0 0 NA 0 100.0 6 2 0 21 #> 22 2 24.0 0 NA 0 9.2 5 1 0 22 #> 23 2 24.0 0 NA 0 49.0 6 2 0 23 #> 24 2 36.0 0 NA 0 8.5 5 1 0 24 #> 25 2 36.0 0 NA 0 32.0 6 2 0 25 #> 26 2 48.0 0 NA 0 6.4 5 1 0 26 #> 27 2 48.0 0 NA 0 26.0 6 2 0 27 #> 28 2 72.0 0 NA 0 4.8 5 1 0 28 #> 29 2 72.0 0 NA 0 22.0 6 2 0 29 #> 30 2 96.0 0 NA 0 3.1 5 1 0 30 #> 31 2 96.0 0 NA 0 28.0 6 2 0 31 #> 32 2 120.0 0 NA 0 2.5 5 1 0 32 #> 33 2 120.0 0 NA 0 33.0 6 2 0 33 #> 34 3 0.0 1 100.0 0 NA 1 0 0 34 #> 35 3 0.0 0 NA 0 100.0 6 2 0 35 #> 36 3 0.5 0 NA 0 0.0 5 1 0 36 #> 37 3 2.0 0 NA 0 8.4 5 1 0 37 #> 38 3 3.0 0 NA 0 9.7 5 1 0 38 #> 39 3 6.0 0 NA 0 9.8 5 1 0 39 #> 40 3 12.0 0 NA 0 11.0 5 1 0 40 #> 41 3 24.0 0 NA 0 8.3 5 1 0 41 #> 42 3 24.0 0 NA 0 46.0 6 2 0 42 #> 43 3 36.0 0 NA 0 7.7 5 1 0 43 #> 44 3 36.0 0 NA 0 22.0 6 2 0 44 #> 45 3 48.0 0 NA 0 6.3 5 1 0 45 #> 46 3 48.0 0 NA 0 19.0 6 2 0 46 #> 47 3 72.0 0 NA 0 4.1 5 1 0 47 #> 48 3 72.0 0 NA 0 20.0 6 2 0 48 #> 49 3 96.0 0 NA 0 3.0 5 1 0 49 #> 50 3 96.0 0 NA 0 42.0 6 2 0 50 #> 51 3 120.0 0 NA 0 1.4 5 1 0 51 #> 52 3 120.0 0 NA 0 49.0 6 2 0 52 #> 53 3 144.0 0 NA 0 54.0 6 2 0 53 #> 54 4 0.0 1 120.0 0 NA 1 0 0 54 #> 55 4 0.0 0 NA 0 100.0 6 2 0 55 #> 56 4 3.0 0 NA 0 12.0 5 1 0 56 #> 57 4 6.0 0 NA 0 13.2 5 1 0 57 #> 58 4 9.0 0 NA 0 14.4 5 1 0 58 #> 59 4 24.0 0 NA 0 9.6 5 1 0 59 #> 60 4 24.0 0 NA 0 30.0 6 2 0 60 #> 61 4 36.0 0 NA 0 8.2 5 1 0 61 #> 62 4 36.0 0 NA 0 24.0 6 2 0 62 #> 63 4 48.0 0 NA 0 7.8 5 1 0 63 #> 64 4 48.0 0 NA 0 13.0 6 2 0 64 #> 65 4 72.0 0 NA 0 5.8 5 1 0 65 #> 66 4 72.0 0 NA 0 9.0 6 2 0 66 #> 67 4 96.0 0 NA 0 4.3 5 1 0 67 #> 68 4 96.0 0 NA 0 9.0 6 2 0 68 #> 69 4 120.0 0 NA 0 3.0 5 1 0 69 #> 70 4 120.0 0 NA 0 11.0 6 2 0 70 #> 71 4 144.0 0 NA 0 12.0 6 2 0 71 #> 72 5 0.0 1 60.0 0 NA 1 0 0 72 #> 73 5 0.0 0 NA 0 82.0 6 2 0 73 #> 74 5 3.0 0 NA 0 11.1 5 1 0 74 #> 75 5 6.0 0 NA 0 11.9 5 1 0 75 #> 76 5 9.0 0 NA 0 9.8 5 1 0 76 #> 77 5 12.0 0 NA 0 11.0 5 1 0 77 #> 78 5 24.0 0 NA 0 8.5 5 1 0 78 #> 79 5 24.0 0 NA 0 43.0 6 2 0 79 #> 80 5 36.0 0 NA 0 7.6 5 1 0 80 #> 81 5 36.0 0 NA 0 25.0 6 2 0 81 #> 82 5 48.0 0 NA 0 5.4 5 1 0 82 #> 83 5 48.0 0 NA 0 18.0 6 2 0 83 #> 84 5 72.0 0 NA 0 4.5 5 1 0 84 #> 85 5 72.0 0 NA 0 17.0 6 2 0 85 #> 86 5 96.0 0 NA 0 3.3 5 1 0 86 #> 87 5 96.0 0 NA 0 23.0 6 2 0 87 #> 88 5 120.0 0 NA 0 2.3 5 1 0 88 #> 89 5 120.0 0 NA 0 29.0 6 2 0 89 #> 90 5 144.0 0 NA 0 41.0 6 2 0 90 #> 91 6 0.0 1 113.0 0 NA 1 0 0 91 #> 92 6 0.0 0 NA 0 100.0 6 2 0 92 #> 93 6 6.0 0 NA 0 8.6 5 1 0 93 #> 94 6 12.0 0 NA 0 8.6 5 1 0 94 #> 95 6 24.0 0 NA 0 7.0 5 1 0 95 #> 96 6 24.0 0 NA 0 34.0 6 2 0 96 #> 97 6 36.0 0 NA 0 5.7 5 1 0 97 #> 98 6 36.0 0 NA 0 23.0 6 2 0 98 #> 99 6 48.0 0 NA 0 4.7 5 1 0 99 #> 100 6 48.0 0 NA 0 20.0 6 2 0 100 #> 101 6 72.0 0 NA 0 3.3 5 1 0 101 #> 102 6 72.0 0 NA 0 16.0 6 2 0 102 #> 103 6 96.0 0 NA 0 2.3 5 1 0 103 #> 104 6 96.0 0 NA 0 17.0 6 2 0 104 #> 105 6 120.0 0 NA 0 1.7 5 1 0 105 #> 106 6 120.0 0 NA 0 18.0 6 2 0 106 #> 107 6 144.0 0 NA 0 25.0 6 2 0 107 #> 108 7 0.0 1 90.0 0 NA 1 0 0 108 #> 109 7 3.0 0 NA 0 13.4 5 1 0 109 #> 110 7 6.0 0 NA 0 12.4 5 1 0 110 #> 111 7 9.0 0 NA 0 12.7 5 1 0 111 #> 112 7 12.0 0 NA 0 8.8 5 1 0 112 #> 113 7 24.0 0 NA 0 6.1 5 1 0 113 #> 114 7 24.0 0 NA 0 36.0 6 2 0 114 #> 115 7 36.0 0 NA 0 3.5 5 1 0 115 #> 116 7 36.0 0 NA 0 33.0 6 2 0 116 #> 117 7 48.0 0 NA 0 1.8 5 1 0 117 #> 118 7 48.0 0 NA 0 28.0 6 2 0 118 #> 119 7 72.0 0 NA 0 1.5 5 1 0 119 #> 120 7 72.0 0 NA 0 52.0 6 2 0 120 #> 121 7 96.0 0 NA 0 1.0 5 1 0 121 #> 122 7 96.0 0 NA 0 80.0 6 2 0 122 #> 123 7 120.0 0 NA 0 90.0 6 2 0 123 #> 124 7 144.0 0 NA 0 100.0 6 2 0 124 #> 125 8 0.0 1 135.0 0 NA 1 0 0 125 #> 126 8 0.0 0 NA 0 88.0 6 2 0 126 #> 127 8 2.0 0 NA 0 17.6 5 1 0 127 #> 128 8 3.0 0 NA 0 17.3 5 1 0 128 #> 129 8 6.0 0 NA 0 15.0 5 1 0 129 #> 130 8 9.0 0 NA 0 15.0 5 1 0 130 #> 131 8 12.0 0 NA 0 12.4 5 1 0 131 #> 132 8 24.0 0 NA 0 7.9 5 1 0 132 #> 133 8 24.0 0 NA 0 35.0 6 2 0 133 #> 134 8 36.0 0 NA 0 7.9 5 1 0 134 #> 135 8 36.0 0 NA 0 20.0 6 2 0 135 #> 136 8 48.0 0 NA 0 5.1 5 1 0 136 #> 137 8 48.0 0 NA 0 12.0 6 2 0 137 #> 138 8 72.0 0 NA 0 3.6 5 1 0 138 #> 139 8 72.0 0 NA 0 16.0 6 2 0 139 #> 140 8 96.0 0 NA 0 2.4 5 1 0 140 #> 141 8 96.0 0 NA 0 23.0 6 2 0 141 #> 142 8 120.0 0 NA 0 2.0 5 1 0 142 #> 143 8 120.0 0 NA 0 36.0 6 2 0 143 #> 144 8 144.0 0 NA 0 48.0 6 2 0 144 #> 145 9 0.0 1 75.0 0 NA 1 0 0 145 #> 146 9 0.0 0 NA 0 92.0 6 2 0 146 #> 147 9 0.5 0 NA 0 0.0 5 1 0 147 #> 148 9 1.0 0 NA 0 1.0 5 1 0 148 #> 149 9 2.0 0 NA 0 4.6 5 1 0 149 #> 150 9 3.0 0 NA 0 12.7 5 1 0 150 #> 151 9 3.0 0 NA 0 8.0 5 1 0 151 #> 152 9 6.0 0 NA 0 12.7 5 1 0 152 #> 153 9 6.0 0 NA 0 11.5 5 1 0 153 #> 154 9 9.0 0 NA 0 12.9 5 1 0 154 #> 155 9 9.0 0 NA 0 11.4 5 1 0 155 #> 156 9 12.0 0 NA 0 11.4 5 1 0 156 #> 157 9 12.0 0 NA 0 11.0 5 1 0 157 #> 158 9 24.0 0 NA 0 9.1 5 1 0 158 #> 159 9 24.0 0 NA 0 33.0 6 2 0 159 #> 160 9 36.0 0 NA 0 8.2 5 1 0 160 #> 161 9 36.0 0 NA 0 22.0 6 2 0 161 #> 162 9 48.0 0 NA 0 5.9 5 1 0 162 #> 163 9 48.0 0 NA 0 16.0 6 2 0 163 #> 164 9 72.0 0 NA 0 3.6 5 1 0 164 #> 165 9 72.0 0 NA 0 18.0 6 2 0 165 #> 166 9 96.0 0 NA 0 1.7 5 1 0 166 #> 167 9 96.0 0 NA 0 32.0 6 2 0 167 #> 168 9 120.0 0 NA 0 1.1 5 1 0 168 #> 169 9 120.0 0 NA 0 30.0 6 2 0 169 #> 170 9 144.0 0 NA 0 45.0 6 2 0 170 #> 171 10 0.0 1 105.0 0 NA 1 0 0 171 #> 172 10 0.0 0 NA 0 90.0 6 2 0 172 #> 173 10 24.0 0 NA 0 8.6 5 1 0 173 #> 174 10 24.0 0 NA 0 39.0 6 2 0 174 #> 175 10 36.0 0 NA 0 8.0 5 1 0 175 #> 176 10 36.0 0 NA 0 22.0 6 2 0 176 #> 177 10 48.0 0 NA 0 6.0 5 1 0 177 #> 178 10 48.0 0 NA 0 17.0 6 2 0 178 #> 179 10 72.0 0 NA 0 4.4 5 1 0 179 #> 180 10 72.0 0 NA 0 17.0 6 2 0 180 #> 181 10 96.0 0 NA 0 3.6 5 1 0 181 #> 182 10 96.0 0 NA 0 22.0 6 2 0 182 #> 183 10 120.0 0 NA 0 2.8 5 1 0 183 #> 184 10 120.0 0 NA 0 25.0 6 2 0 184 #> 185 10 144.0 0 NA 0 33.0 6 2 0 185 #> 186 11 0.0 1 123.0 0 NA 1 0 0 186 #> 187 11 0.0 0 NA 0 100.0 6 2 0 187 #> 188 11 1.5 0 NA 0 11.4 5 1 0 188 #> 189 11 3.0 0 NA 0 15.4 5 1 0 189 #> 190 11 6.0 0 NA 0 17.5 5 1 0 190 #> 191 11 12.0 0 NA 0 14.0 5 1 0 191 #> 192 11 24.0 0 NA 0 9.0 5 1 0 192 #> 193 11 24.0 0 NA 0 37.0 6 2 0 193 #> 194 11 36.0 0 NA 0 8.9 5 1 0 194 #> 195 11 36.0 0 NA 0 24.0 6 2 0 195 #> 196 11 48.0 0 NA 0 6.6 5 1 0 196 #> 197 11 48.0 0 NA 0 14.0 6 2 0 197 #> 198 11 72.0 0 NA 0 4.2 5 1 0 198 #> 199 11 72.0 0 NA 0 11.0 6 2 0 199 #> 200 11 96.0 0 NA 0 3.6 5 1 0 200 #> 201 11 96.0 0 NA 0 14.0 6 2 0 201 #> 202 11 120.0 0 NA 0 2.6 5 1 0 202 #> 203 11 120.0 0 NA 0 23.0 6 2 0 203 #> 204 11 144.0 0 NA 0 33.0 6 2 0 204 #> 205 12 0.0 1 113.0 0 NA 1 0 0 205 #> 206 12 0.0 0 NA 0 85.0 6 2 0 206 #> 207 12 1.5 0 NA 0 0.6 5 1 0 207 #> 208 12 3.0 0 NA 0 2.8 5 1 0 208 #> 209 12 6.0 0 NA 0 13.8 5 1 0 209 #> 210 12 9.0 0 NA 0 15.0 5 1 0 210 #> 211 12 24.0 0 NA 0 10.5 5 1 0 211 #> 212 12 24.0 0 NA 0 25.0 6 2 0 212 #> 213 12 36.0 0 NA 0 9.1 5 1 0 213 #> 214 12 36.0 0 NA 0 15.0 6 2 0 214 #> 215 12 48.0 0 NA 0 6.6 5 1 0 215 #> 216 12 48.0 0 NA 0 11.0 6 2 0 216 #> 217 12 72.0 0 NA 0 4.9 5 1 0 217 #> 218 12 96.0 0 NA 0 2.4 5 1 0 218 #> 219 12 120.0 0 NA 0 1.9 5 1 0 219 #> 220 13 0.0 1 113.0 0 NA 1 0 0 220 #> 221 13 0.0 0 NA 0 88.0 6 2 0 221 #> 222 13 1.5 0 NA 0 3.6 5 1 0 222 #> 223 13 3.0 0 NA 0 12.9 5 1 0 223 #> 224 13 6.0 0 NA 0 12.9 5 1 0 224 #> 225 13 9.0 0 NA 0 10.2 5 1 0 225 #> 226 13 24.0 0 NA 0 6.4 5 1 0 226 #> 227 13 24.0 0 NA 0 41.0 6 2 0 227 #> 228 13 36.0 0 NA 0 6.9 5 1 0 228 #> 229 13 36.0 0 NA 0 23.0 6 2 0 229 #> 230 13 48.0 0 NA 0 4.5 5 1 0 230 #> 231 13 48.0 0 NA 0 16.0 6 2 0 231 #> 232 13 72.0 0 NA 0 3.2 5 1 0 232 #> 233 13 72.0 0 NA 0 14.0 6 2 0 233 #> 234 13 96.0 0 NA 0 2.4 5 1 0 234 #> 235 13 96.0 0 NA 0 18.0 6 2 0 235 #> 236 13 120.0 0 NA 0 1.3 5 1 0 236 #> 237 13 120.0 0 NA 0 22.0 6 2 0 237 #> 238 13 144.0 0 NA 0 35.0 6 2 0 238 #> 239 14 0.0 1 75.0 0 NA 1 0 0 239 #> 240 14 0.0 0 NA 0 85.0 6 2 0 240 #> 241 14 0.5 0 NA 0 0.0 5 1 0 241 #> 242 14 1.0 0 NA 0 2.7 5 1 0 242 #> 243 14 2.0 0 NA 0 11.6 5 1 0 243 #> 244 14 3.0 0 NA 0 11.6 5 1 0 244 #> 245 14 6.0 0 NA 0 11.3 5 1 0 245 #> 246 14 9.0 0 NA 0 9.7 5 1 0 246 #> 247 14 24.0 0 NA 0 6.5 5 1 0 247 #> 248 14 24.0 0 NA 0 32.0 6 2 0 248 #> 249 14 36.0 0 NA 0 5.2 5 1 0 249 #> 250 14 36.0 0 NA 0 22.0 6 2 0 250 #> 251 14 48.0 0 NA 0 3.6 5 1 0 251 #> 252 14 48.0 0 NA 0 21.0 6 2 0 252 #> 253 14 72.0 0 NA 0 2.4 5 1 0 253 #> 254 14 72.0 0 NA 0 28.0 6 2 0 254 #> 255 14 96.0 0 NA 0 0.9 5 1 0 255 #> 256 14 96.0 0 NA 0 38.0 6 2 0 256 #> 257 14 120.0 0 NA 0 46.0 6 2 0 257 #> 258 14 144.0 0 NA 0 65.0 6 2 0 258 #> 259 15 0.0 1 85.0 0 NA 1 0 0 259 #> 260 15 0.0 0 NA 0 100.0 6 2 0 260 #> 261 15 1.0 0 NA 0 6.6 5 1 0 261 #> 262 15 3.0 0 NA 0 11.9 5 1 0 262 #> 263 15 6.0 0 NA 0 11.7 5 1 0 263 #> 264 15 9.0 0 NA 0 12.2 5 1 0 264 #> 265 15 24.0 0 NA 0 8.1 5 1 0 265 #> 266 15 24.0 0 NA 0 43.0 6 2 0 266 #> 267 15 36.0 0 NA 0 7.4 5 1 0 267 #> 268 15 36.0 0 NA 0 26.0 6 2 0 268 #> 269 15 48.0 0 NA 0 6.8 5 1 0 269 #> 270 15 48.0 0 NA 0 15.0 6 2 0 270 #> 271 15 72.0 0 NA 0 5.3 5 1 0 271 #> 272 15 72.0 0 NA 0 13.0 6 2 0 272 #> 273 15 96.0 0 NA 0 3.0 5 1 0 273 #> 274 15 96.0 0 NA 0 21.0 6 2 0 274 #> 275 15 120.0 0 NA 0 2.0 5 1 0 275 #> 276 15 120.0 0 NA 0 28.0 6 2 0 276 #> 277 15 144.0 0 NA 0 39.0 6 2 0 277 #> 278 16 0.0 1 87.0 0 NA 1 0 0 278 #> 279 16 0.0 0 NA 0 100.0 6 2 0 279 #> 280 16 24.0 0 NA 0 10.4 5 1 0 280 #> 281 16 24.0 0 NA 0 42.0 6 2 0 281 #> 282 16 36.0 0 NA 0 8.9 5 1 0 282 #> 283 16 36.0 0 NA 0 32.0 6 2 0 283 #> 284 16 48.0 0 NA 0 7.0 5 1 0 284 #> 285 16 48.0 0 NA 0 26.0 6 2 0 285 #> 286 16 72.0 0 NA 0 4.4 5 1 0 286 #> 287 16 72.0 0 NA 0 31.0 6 2 0 287 #> 288 16 96.0 0 NA 0 3.2 5 1 0 288 #> 289 16 96.0 0 NA 0 33.0 6 2 0 289 #> 290 16 120.0 0 NA 0 2.4 5 1 0 290 #> 291 16 120.0 0 NA 0 54.0 6 2 0 291 #> 292 17 0.0 1 117.0 0 NA 1 0 0 292 #> 293 17 0.0 0 NA 0 100.0 6 2 0 293 #> 294 17 24.0 0 NA 0 7.6 5 1 0 294 #> 295 17 24.0 0 NA 0 35.0 6 2 0 295 #> 296 17 36.0 0 NA 0 6.4 5 1 0 296 #> 297 17 36.0 0 NA 0 23.0 6 2 0 297 #> 298 17 48.0 0 NA 0 6.0 5 1 0 298 #> 299 17 48.0 0 NA 0 17.0 6 2 0 299 #> 300 17 72.0 0 NA 0 4.0 5 1 0 300 #> 301 17 72.0 0 NA 0 18.0 6 2 0 301 #> 302 17 96.0 0 NA 0 3.1 5 1 0 302 #> 303 17 96.0 0 NA 0 18.0 6 2 0 303 #> 304 17 120.0 0 NA 0 2.0 5 1 0 304 #> 305 17 120.0 0 NA 0 21.0 6 2 0 305 #> 306 18 0.0 1 112.0 0 NA 1 0 0 306 #> 307 18 0.0 0 NA 0 100.0 6 2 0 307 #> 308 18 24.0 0 NA 0 7.6 5 1 0 308 #> 309 18 24.0 0 NA 0 32.0 6 2 0 309 #> 310 18 36.0 0 NA 0 6.6 5 1 0 310 #> 311 18 36.0 0 NA 0 20.0 6 2 0 311 #> 312 18 48.0 0 NA 0 5.4 5 1 0 312 #> 313 18 48.0 0 NA 0 18.0 6 2 0 313 #> 314 18 72.0 0 NA 0 3.4 5 1 0 314 #> 315 18 72.0 0 NA 0 18.0 6 2 0 315 #> 316 18 96.0 0 NA 0 1.2 5 1 0 316 #> 317 18 96.0 0 NA 0 19.0 6 2 0 317 #> 318 18 120.0 0 NA 0 0.9 5 1 0 318 #> 319 18 120.0 0 NA 0 29.0 6 2 0 319 #> 320 19 0.0 1 95.5 0 NA 1 0 0 320 #> 321 19 0.0 0 NA 0 100.0 6 2 0 321 #> 322 19 24.0 0 NA 0 6.6 5 1 0 322 #> 323 19 24.0 0 NA 0 33.0 6 2 0 323 #> 324 19 36.0 0 NA 0 5.3 5 1 0 324 #> 325 19 36.0 0 NA 0 28.0 6 2 0 325 #> 326 19 48.0 0 NA 0 3.6 5 1 0 326 #> 327 19 48.0 0 NA 0 18.0 6 2 0 327 #> 328 19 72.0 0 NA 0 2.7 5 1 0 328 #> 329 19 72.0 0 NA 0 18.0 6 2 0 329 #> 330 19 96.0 0 NA 0 1.4 5 1 0 330 #> 331 19 96.0 0 NA 0 17.0 6 2 0 331 #> 332 19 120.0 0 NA 0 1.1 5 1 0 332 #> 333 19 120.0 0 NA 0 26.0 6 2 0 333 #> 334 20 0.0 1 88.5 0 NA 1 0 0 334 #> 335 20 0.0 0 NA 0 100.0 6 2 0 335 #> 336 20 24.0 0 NA 0 9.6 5 1 0 336 #> 337 20 24.0 0 NA 0 41.0 6 2 0 337 #> 338 20 36.0 0 NA 0 8.0 5 1 0 338 #> 339 20 36.0 0 NA 0 30.0 6 2 0 339 #> 340 20 48.0 0 NA 0 6.6 5 1 0 340 #> 341 20 48.0 0 NA 0 22.0 6 2 0 341 #> 342 20 72.0 0 NA 0 5.6 5 1 0 342 #> 343 20 72.0 0 NA 0 23.0 6 2 0 343 #> 344 20 96.0 0 NA 0 3.5 5 1 0 344 #> 345 20 96.0 0 NA 0 23.0 6 2 0 345 #> 346 20 120.0 0 NA 0 2.3 5 1 0 346 #> 347 20 120.0 0 NA 0 35.0 6 2 0 347 #> 348 21 0.0 1 93.0 0 NA 1 0 0 348 #> 349 21 0.0 0 NA 0 100.0 6 2 0 349 #> 350 21 24.0 0 NA 0 7.3 5 1 0 350 #> 351 21 24.0 0 NA 0 46.0 6 2 0 351 #> 352 21 36.0 0 NA 0 6.1 5 1 0 352 #> 353 21 36.0 0 NA 0 27.0 6 2 0 353 #> 354 21 48.0 0 NA 0 4.3 5 1 0 354 #> 355 21 48.0 0 NA 0 22.0 6 2 0 355 #> 356 21 72.0 0 NA 0 3.2 5 1 0 356 #> 357 21 72.0 0 NA 0 36.0 6 2 0 357 #> 358 21 96.0 0 NA 0 2.3 5 1 0 358 #> 359 21 96.0 0 NA 0 40.0 6 2 0 359 #> 360 21 120.0 0 NA 0 1.9 5 1 0 360 #> 361 21 120.0 0 NA 0 44.0 6 2 0 361 #> 362 22 0.0 1 87.0 0 NA 1 0 0 362 #> 363 22 0.0 0 NA 0 100.0 6 2 0 363 #> 364 22 24.0 0 NA 0 8.9 5 1 0 364 #> 365 22 24.0 0 NA 0 35.0 6 2 0 365 #> 366 22 36.0 0 NA 0 8.4 5 1 0 366 #> 367 22 36.0 0 NA 0 27.0 6 2 0 367 #> 368 22 48.0 0 NA 0 8.0 5 1 0 368 #> 369 22 48.0 0 NA 0 23.0 6 2 0 369 #> 370 22 72.0 0 NA 0 4.4 5 1 0 370 #> 371 22 72.0 0 NA 0 27.0 6 2 0 371 #> 372 22 96.0 0 NA 0 3.2 5 1 0 372 #> 373 22 96.0 0 NA 0 43.0 6 2 0 373 #> 374 22 120.0 0 NA 0 1.7 5 1 0 374 #> 375 22 120.0 0 NA 0 43.0 6 2 0 375 #> 376 23 0.0 1 110.0 0 NA 1 0 0 376 #> 377 23 0.0 0 NA 0 100.0 6 2 0 377 #> 378 23 24.0 0 NA 0 9.8 5 1 0 378 #> 379 23 24.0 0 NA 0 34.0 6 2 0 379 #> 380 23 36.0 0 NA 0 8.4 5 1 0 380 #> 381 23 36.0 0 NA 0 24.0 6 2 0 381 #> 382 23 48.0 0 NA 0 6.6 5 1 0 382 #> 383 23 48.0 0 NA 0 15.0 6 2 0 383 #> 384 23 72.0 0 NA 0 4.8 5 1 0 384 #> 385 23 72.0 0 NA 0 15.0 6 2 0 385 #> 386 23 96.0 0 NA 0 3.2 5 1 0 386 #> 387 23 96.0 0 NA 0 19.0 6 2 0 387 #> 388 23 120.0 0 NA 0 2.4 5 1 0 388 #> 389 23 120.0 0 NA 0 19.0 6 2 0 389 #> 390 24 0.0 1 115.0 0 NA 1 0 0 390 #> 391 24 0.0 0 NA 0 88.0 6 2 0 391 #> 392 24 24.0 0 NA 0 8.2 5 1 0 392 #> 393 24 24.0 0 NA 0 37.0 6 2 0 393 #> 394 24 36.0 0 NA 0 7.5 5 1 0 394 #> 395 24 36.0 0 NA 0 20.0 6 2 0 395 #> 396 24 48.0 0 NA 0 6.8 5 1 0 396 #> 397 24 48.0 0 NA 0 20.0 6 2 0 397 #> 398 24 72.0 0 NA 0 5.5 5 1 0 398 #> 399 24 72.0 0 NA 0 26.0 6 2 0 399 #> 400 24 96.0 0 NA 0 4.5 5 1 0 400 #> 401 24 96.0 0 NA 0 28.0 6 2 0 401 #> 402 24 120.0 0 NA 0 3.7 5 1 0 402 #> 403 24 120.0 0 NA 0 50.0 6 2 0 403 #> 404 25 0.0 1 112.0 0 NA 1 0 0 404 #> 405 25 0.0 0 NA 0 100.0 6 2 0 405 #> 406 25 24.0 0 NA 0 11.0 5 1 0 406 #> 407 25 24.0 0 NA 0 32.0 6 2 0 407 #> 408 25 36.0 0 NA 0 10.0 5 1 0 408 #> 409 25 36.0 0 NA 0 20.0 6 2 0 409 #> 410 25 48.0 0 NA 0 8.2 5 1 0 410 #> 411 25 48.0 0 NA 0 17.0 6 2 0 411 #> 412 25 72.0 0 NA 0 6.0 5 1 0 412 #> 413 25 72.0 0 NA 0 19.0 6 2 0 413 #> 414 25 96.0 0 NA 0 3.7 5 1 0 414 #> 415 25 96.0 0 NA 0 21.0 6 2 0 415 #> 416 25 120.0 0 NA 0 2.6 5 1 0 416 #> 417 25 120.0 0 NA 0 30.0 6 2 0 417 #> 418 26 0.0 1 120.0 0 NA 1 0 0 418 #> 419 26 0.0 0 NA 0 100.0 6 2 0 419 #> 420 26 24.0 0 NA 0 10.0 5 1 0 420 #> 421 26 24.0 0 NA 0 41.0 6 2 0 421 #> 422 26 36.0 0 NA 0 9.0 5 1 0 422 #> 423 26 36.0 0 NA 0 28.0 6 2 0 423 #> 424 26 48.0 0 NA 0 7.3 5 1 0 424 #> 425 26 48.0 0 NA 0 19.0 6 2 0 425 #> 426 26 72.0 0 NA 0 5.2 5 1 0 426 #> 427 26 72.0 0 NA 0 17.0 6 2 0 427 #> 428 26 96.0 0 NA 0 3.7 5 1 0 428 #> 429 26 96.0 0 NA 0 17.0 6 2 0 429 #> 430 26 120.0 0 NA 0 2.7 5 1 0 430 #> 431 26 120.0 0 NA 0 24.0 6 2 0 431 #> 432 27 0.0 1 120.0 0 NA 1 0 0 432 #> 433 27 0.0 0 NA 0 100.0 6 2 0 433 #> 434 27 24.0 0 NA 0 11.8 5 1 0 434 #> 435 27 24.0 0 NA 0 32.0 6 2 0 435 #> 436 27 36.0 0 NA 0 9.2 5 1 0 436 #> 437 27 36.0 0 NA 0 21.0 6 2 0 437 #> 438 27 48.0 0 NA 0 7.7 5 1 0 438 #> 439 27 48.0 0 NA 0 19.0 6 2 0 439 #> 440 27 72.0 0 NA 0 4.9 5 1 0 440 #> 441 27 72.0 0 NA 0 22.0 6 2 0 441 #> 442 27 96.0 0 NA 0 3.4 5 1 0 442 #> 443 27 96.0 0 NA 0 33.0 6 2 0 443 #> 444 27 120.0 0 NA 0 2.7 5 1 0 444 #> 445 27 120.0 0 NA 0 46.0 6 2 0 445 #> 446 28 0.0 1 120.0 0 NA 1 0 0 446 #> 447 28 0.0 0 NA 0 100.0 6 2 0 447 #> 448 28 24.0 0 NA 0 10.1 5 1 0 448 #> 449 28 24.0 0 NA 0 39.0 6 2 0 449 #> 450 28 36.0 0 NA 0 8.0 5 1 0 450 #> 451 28 36.0 0 NA 0 25.0 6 2 0 451 #> 452 28 48.0 0 NA 0 6.0 5 1 0 452 #> 453 28 48.0 0 NA 0 16.0 6 2 0 453 #> 454 28 72.0 0 NA 0 4.9 5 1 0 454 #> 455 28 72.0 0 NA 0 14.0 6 2 0 455 #> 456 28 96.0 0 NA 0 3.4 5 1 0 456 #> 457 28 96.0 0 NA 0 15.0 6 2 0 457 #> 458 28 120.0 0 NA 0 2.0 5 1 0 458 #> 459 28 120.0 0 NA 0 20.0 6 2 0 459 #> 460 29 0.0 1 153.0 0 NA 1 0 0 460 #> 461 29 0.0 0 NA 0 86.0 6 2 0 461 #> 462 29 24.0 0 NA 0 8.3 5 1 0 462 #> 463 29 24.0 0 NA 0 35.0 6 2 0 463 #> 464 29 36.0 0 NA 0 7.0 5 1 0 464 #> 465 29 36.0 0 NA 0 21.0 6 2 0 465 #> 466 29 48.0 0 NA 0 5.6 5 1 0 466 #> 467 29 48.0 0 NA 0 18.0 6 2 0 467 #> 468 29 72.0 0 NA 0 4.1 5 1 0 468 #> 469 29 72.0 0 NA 0 20.0 6 2 0 469 #> 470 29 96.0 0 NA 0 3.1 5 1 0 470 #> 471 29 96.0 0 NA 0 29.0 6 2 0 471 #> 472 29 120.0 0 NA 0 2.2 5 1 0 472 #> 473 29 120.0 0 NA 0 41.0 6 2 0 473 #> 474 30 0.0 1 105.0 0 NA 1 0 0 474 #> 475 30 0.0 0 NA 0 100.0 6 2 0 475 #> 476 30 24.0 0 NA 0 9.9 5 1 0 476 #> 477 30 24.0 0 NA 0 45.0 6 2 0 477 #> 478 30 36.0 0 NA 0 7.5 5 1 0 478 #> 479 30 36.0 0 NA 0 24.0 6 2 0 479 #> 480 30 48.0 0 NA 0 6.5 5 1 0 480 #> 481 30 48.0 0 NA 0 23.0 6 2 0 481 #> 482 30 72.0 0 NA 0 4.1 5 1 0 482 #> 483 30 72.0 0 NA 0 26.0 6 2 0 483 #> 484 30 96.0 0 NA 0 2.9 5 1 0 484 #> 485 30 96.0 0 NA 0 28.0 6 2 0 485 #> 486 30 120.0 0 NA 0 2.3 5 1 0 486 #> 487 30 120.0 0 NA 0 39.0 6 2 0 487 #> 488 31 0.0 1 125.0 0 NA 1 0 0 488 #> 489 31 0.0 0 NA 0 100.0 6 2 0 489 #> 490 31 24.0 0 NA 0 9.5 5 1 0 490 #> 491 31 24.0 0 NA 0 45.0 6 2 0 491 #> 492 31 36.0 0 NA 0 7.8 5 1 0 492 #> 493 31 36.0 0 NA 0 30.0 6 2 0 493 #> 494 31 48.0 0 NA 0 6.4 5 1 0 494 #> 495 31 48.0 0 NA 0 24.0 6 2 0 495 #> 496 31 72.0 0 NA 0 4.5 5 1 0 496 #> 497 31 72.0 0 NA 0 22.0 6 2 0 497 #> 498 31 96.0 0 NA 0 3.4 5 1 0 498 #> 499 31 96.0 0 NA 0 28.0 6 2 0 499 #> 500 31 120.0 0 NA 0 2.5 5 1 0 500 #> 501 31 120.0 0 NA 0 42.0 6 2 0 501 #> 502 32 0.0 1 93.0 0 NA 1 0 0 502 #> 503 32 0.0 0 NA 0 100.0 6 2 0 503 #> 504 32 24.0 0 NA 0 8.9 5 1 0 504 #> 505 32 24.0 0 NA 0 36.0 6 2 0 505 #> 506 32 36.0 0 NA 0 7.7 5 1 0 506 #> 507 32 36.0 0 NA 0 27.0 6 2 0 507 #> 508 32 48.0 0 NA 0 6.9 5 1 0 508 #> 509 32 48.0 0 NA 0 24.0 6 2 0 509 #> 510 32 72.0 0 NA 0 4.4 5 1 0 510 #> 511 32 72.0 0 NA 0 23.0 6 2 0 511 #> 512 32 96.0 0 NA 0 3.5 5 1 0 512 #> 513 32 96.0 0 NA 0 20.0 6 2 0 513 #> 514 32 120.0 0 NA 0 2.5 5 1 0 514 #> 515 32 120.0 0 NA 0 22.0 6 2 0 515 bblDatToRxode(pk.turnover.emax3, nlmixr2data::warfarin) #> #> #> ID TIME EVID AMT II DV CMT DVID SS nlmixrRowNums #> 1 1 0.0 1 100.0 0 NA 1 0 0 1 #> 2 1 0.5 0 NA 0 0.0 5 1 0 2 #> 3 1 1.0 0 NA 0 1.9 5 1 0 3 #> 4 1 2.0 0 NA 0 3.3 5 1 0 4 #> 5 1 3.0 0 NA 0 6.6 5 1 0 5 #> 6 1 6.0 0 NA 0 9.1 5 1 0 6 #> 7 1 9.0 0 NA 0 10.8 5 1 0 7 #> 8 1 12.0 0 NA 0 8.6 5 1 0 8 #> 9 1 24.0 0 NA 0 5.6 5 1 0 9 #> 10 1 24.0 0 NA 0 44.0 6 2 0 10 #> 11 1 36.0 0 NA 0 4.0 5 1 0 11 #> 12 1 36.0 0 NA 0 27.0 6 2 0 12 #> 13 1 48.0 0 NA 0 2.7 5 1 0 13 #> 14 1 48.0 0 NA 0 28.0 6 2 0 14 #> 15 1 72.0 0 NA 0 0.8 5 1 0 15 #> 16 1 72.0 0 NA 0 31.0 6 2 0 16 #> 17 1 96.0 0 NA 0 60.0 6 2 0 17 #> 18 1 120.0 0 NA 0 65.0 6 2 0 18 #> 19 1 144.0 0 NA 0 71.0 6 2 0 19 #> 20 2 0.0 1 100.0 0 NA 1 0 0 20 #> 21 2 0.0 0 NA 0 100.0 6 2 0 21 #> 22 2 24.0 0 NA 0 9.2 5 1 0 22 #> 23 2 24.0 0 NA 0 49.0 6 2 0 23 #> 24 2 36.0 0 NA 0 8.5 5 1 0 24 #> 25 2 36.0 0 NA 0 32.0 6 2 0 25 #> 26 2 48.0 0 NA 0 6.4 5 1 0 26 #> 27 2 48.0 0 NA 0 26.0 6 2 0 27 #> 28 2 72.0 0 NA 0 4.8 5 1 0 28 #> 29 2 72.0 0 NA 0 22.0 6 2 0 29 #> 30 2 96.0 0 NA 0 3.1 5 1 0 30 #> 31 2 96.0 0 NA 0 28.0 6 2 0 31 #> 32 2 120.0 0 NA 0 2.5 5 1 0 32 #> 33 2 120.0 0 NA 0 33.0 6 2 0 33 #> 34 3 0.0 1 100.0 0 NA 1 0 0 34 #> 35 3 0.0 0 NA 0 100.0 6 2 0 35 #> 36 3 0.5 0 NA 0 0.0 5 1 0 36 #> 37 3 2.0 0 NA 0 8.4 5 1 0 37 #> 38 3 3.0 0 NA 0 9.7 5 1 0 38 #> 39 3 6.0 0 NA 0 9.8 5 1 0 39 #> 40 3 12.0 0 NA 0 11.0 5 1 0 40 #> 41 3 24.0 0 NA 0 8.3 5 1 0 41 #> 42 3 24.0 0 NA 0 46.0 6 2 0 42 #> 43 3 36.0 0 NA 0 7.7 5 1 0 43 #> 44 3 36.0 0 NA 0 22.0 6 2 0 44 #> 45 3 48.0 0 NA 0 6.3 5 1 0 45 #> 46 3 48.0 0 NA 0 19.0 6 2 0 46 #> 47 3 72.0 0 NA 0 4.1 5 1 0 47 #> 48 3 72.0 0 NA 0 20.0 6 2 0 48 #> 49 3 96.0 0 NA 0 3.0 5 1 0 49 #> 50 3 96.0 0 NA 0 42.0 6 2 0 50 #> 51 3 120.0 0 NA 0 1.4 5 1 0 51 #> 52 3 120.0 0 NA 0 49.0 6 2 0 52 #> 53 3 144.0 0 NA 0 54.0 6 2 0 53 #> 54 4 0.0 1 120.0 0 NA 1 0 0 54 #> 55 4 0.0 0 NA 0 100.0 6 2 0 55 #> 56 4 3.0 0 NA 0 12.0 5 1 0 56 #> 57 4 6.0 0 NA 0 13.2 5 1 0 57 #> 58 4 9.0 0 NA 0 14.4 5 1 0 58 #> 59 4 24.0 0 NA 0 9.6 5 1 0 59 #> 60 4 24.0 0 NA 0 30.0 6 2 0 60 #> 61 4 36.0 0 NA 0 8.2 5 1 0 61 #> 62 4 36.0 0 NA 0 24.0 6 2 0 62 #> 63 4 48.0 0 NA 0 7.8 5 1 0 63 #> 64 4 48.0 0 NA 0 13.0 6 2 0 64 #> 65 4 72.0 0 NA 0 5.8 5 1 0 65 #> 66 4 72.0 0 NA 0 9.0 6 2 0 66 #> 67 4 96.0 0 NA 0 4.3 5 1 0 67 #> 68 4 96.0 0 NA 0 9.0 6 2 0 68 #> 69 4 120.0 0 NA 0 3.0 5 1 0 69 #> 70 4 120.0 0 NA 0 11.0 6 2 0 70 #> 71 4 144.0 0 NA 0 12.0 6 2 0 71 #> 72 5 0.0 1 60.0 0 NA 1 0 0 72 #> 73 5 0.0 0 NA 0 82.0 6 2 0 73 #> 74 5 3.0 0 NA 0 11.1 5 1 0 74 #> 75 5 6.0 0 NA 0 11.9 5 1 0 75 #> 76 5 9.0 0 NA 0 9.8 5 1 0 76 #> 77 5 12.0 0 NA 0 11.0 5 1 0 77 #> 78 5 24.0 0 NA 0 8.5 5 1 0 78 #> 79 5 24.0 0 NA 0 43.0 6 2 0 79 #> 80 5 36.0 0 NA 0 7.6 5 1 0 80 #> 81 5 36.0 0 NA 0 25.0 6 2 0 81 #> 82 5 48.0 0 NA 0 5.4 5 1 0 82 #> 83 5 48.0 0 NA 0 18.0 6 2 0 83 #> 84 5 72.0 0 NA 0 4.5 5 1 0 84 #> 85 5 72.0 0 NA 0 17.0 6 2 0 85 #> 86 5 96.0 0 NA 0 3.3 5 1 0 86 #> 87 5 96.0 0 NA 0 23.0 6 2 0 87 #> 88 5 120.0 0 NA 0 2.3 5 1 0 88 #> 89 5 120.0 0 NA 0 29.0 6 2 0 89 #> 90 5 144.0 0 NA 0 41.0 6 2 0 90 #> 91 6 0.0 1 113.0 0 NA 1 0 0 91 #> 92 6 0.0 0 NA 0 100.0 6 2 0 92 #> 93 6 6.0 0 NA 0 8.6 5 1 0 93 #> 94 6 12.0 0 NA 0 8.6 5 1 0 94 #> 95 6 24.0 0 NA 0 7.0 5 1 0 95 #> 96 6 24.0 0 NA 0 34.0 6 2 0 96 #> 97 6 36.0 0 NA 0 5.7 5 1 0 97 #> 98 6 36.0 0 NA 0 23.0 6 2 0 98 #> 99 6 48.0 0 NA 0 4.7 5 1 0 99 #> 100 6 48.0 0 NA 0 20.0 6 2 0 100 #> 101 6 72.0 0 NA 0 3.3 5 1 0 101 #> 102 6 72.0 0 NA 0 16.0 6 2 0 102 #> 103 6 96.0 0 NA 0 2.3 5 1 0 103 #> 104 6 96.0 0 NA 0 17.0 6 2 0 104 #> 105 6 120.0 0 NA 0 1.7 5 1 0 105 #> 106 6 120.0 0 NA 0 18.0 6 2 0 106 #> 107 6 144.0 0 NA 0 25.0 6 2 0 107 #> 108 7 0.0 1 90.0 0 NA 1 0 0 108 #> 109 7 3.0 0 NA 0 13.4 5 1 0 109 #> 110 7 6.0 0 NA 0 12.4 5 1 0 110 #> 111 7 9.0 0 NA 0 12.7 5 1 0 111 #> 112 7 12.0 0 NA 0 8.8 5 1 0 112 #> 113 7 24.0 0 NA 0 6.1 5 1 0 113 #> 114 7 24.0 0 NA 0 36.0 6 2 0 114 #> 115 7 36.0 0 NA 0 3.5 5 1 0 115 #> 116 7 36.0 0 NA 0 33.0 6 2 0 116 #> 117 7 48.0 0 NA 0 1.8 5 1 0 117 #> 118 7 48.0 0 NA 0 28.0 6 2 0 118 #> 119 7 72.0 0 NA 0 1.5 5 1 0 119 #> 120 7 72.0 0 NA 0 52.0 6 2 0 120 #> 121 7 96.0 0 NA 0 1.0 5 1 0 121 #> 122 7 96.0 0 NA 0 80.0 6 2 0 122 #> 123 7 120.0 0 NA 0 90.0 6 2 0 123 #> 124 7 144.0 0 NA 0 100.0 6 2 0 124 #> 125 8 0.0 1 135.0 0 NA 1 0 0 125 #> 126 8 0.0 0 NA 0 88.0 6 2 0 126 #> 127 8 2.0 0 NA 0 17.6 5 1 0 127 #> 128 8 3.0 0 NA 0 17.3 5 1 0 128 #> 129 8 6.0 0 NA 0 15.0 5 1 0 129 #> 130 8 9.0 0 NA 0 15.0 5 1 0 130 #> 131 8 12.0 0 NA 0 12.4 5 1 0 131 #> 132 8 24.0 0 NA 0 7.9 5 1 0 132 #> 133 8 24.0 0 NA 0 35.0 6 2 0 133 #> 134 8 36.0 0 NA 0 7.9 5 1 0 134 #> 135 8 36.0 0 NA 0 20.0 6 2 0 135 #> 136 8 48.0 0 NA 0 5.1 5 1 0 136 #> 137 8 48.0 0 NA 0 12.0 6 2 0 137 #> 138 8 72.0 0 NA 0 3.6 5 1 0 138 #> 139 8 72.0 0 NA 0 16.0 6 2 0 139 #> 140 8 96.0 0 NA 0 2.4 5 1 0 140 #> 141 8 96.0 0 NA 0 23.0 6 2 0 141 #> 142 8 120.0 0 NA 0 2.0 5 1 0 142 #> 143 8 120.0 0 NA 0 36.0 6 2 0 143 #> 144 8 144.0 0 NA 0 48.0 6 2 0 144 #> 145 9 0.0 1 75.0 0 NA 1 0 0 145 #> 146 9 0.0 0 NA 0 92.0 6 2 0 146 #> 147 9 0.5 0 NA 0 0.0 5 1 0 147 #> 148 9 1.0 0 NA 0 1.0 5 1 0 148 #> 149 9 2.0 0 NA 0 4.6 5 1 0 149 #> 150 9 3.0 0 NA 0 12.7 5 1 0 150 #> 151 9 3.0 0 NA 0 8.0 5 1 0 151 #> 152 9 6.0 0 NA 0 12.7 5 1 0 152 #> 153 9 6.0 0 NA 0 11.5 5 1 0 153 #> 154 9 9.0 0 NA 0 12.9 5 1 0 154 #> 155 9 9.0 0 NA 0 11.4 5 1 0 155 #> 156 9 12.0 0 NA 0 11.4 5 1 0 156 #> 157 9 12.0 0 NA 0 11.0 5 1 0 157 #> 158 9 24.0 0 NA 0 9.1 5 1 0 158 #> 159 9 24.0 0 NA 0 33.0 6 2 0 159 #> 160 9 36.0 0 NA 0 8.2 5 1 0 160 #> 161 9 36.0 0 NA 0 22.0 6 2 0 161 #> 162 9 48.0 0 NA 0 5.9 5 1 0 162 #> 163 9 48.0 0 NA 0 16.0 6 2 0 163 #> 164 9 72.0 0 NA 0 3.6 5 1 0 164 #> 165 9 72.0 0 NA 0 18.0 6 2 0 165 #> 166 9 96.0 0 NA 0 1.7 5 1 0 166 #> 167 9 96.0 0 NA 0 32.0 6 2 0 167 #> 168 9 120.0 0 NA 0 1.1 5 1 0 168 #> 169 9 120.0 0 NA 0 30.0 6 2 0 169 #> 170 9 144.0 0 NA 0 45.0 6 2 0 170 #> 171 10 0.0 1 105.0 0 NA 1 0 0 171 #> 172 10 0.0 0 NA 0 90.0 6 2 0 172 #> 173 10 24.0 0 NA 0 8.6 5 1 0 173 #> 174 10 24.0 0 NA 0 39.0 6 2 0 174 #> 175 10 36.0 0 NA 0 8.0 5 1 0 175 #> 176 10 36.0 0 NA 0 22.0 6 2 0 176 #> 177 10 48.0 0 NA 0 6.0 5 1 0 177 #> 178 10 48.0 0 NA 0 17.0 6 2 0 178 #> 179 10 72.0 0 NA 0 4.4 5 1 0 179 #> 180 10 72.0 0 NA 0 17.0 6 2 0 180 #> 181 10 96.0 0 NA 0 3.6 5 1 0 181 #> 182 10 96.0 0 NA 0 22.0 6 2 0 182 #> 183 10 120.0 0 NA 0 2.8 5 1 0 183 #> 184 10 120.0 0 NA 0 25.0 6 2 0 184 #> 185 10 144.0 0 NA 0 33.0 6 2 0 185 #> 186 11 0.0 1 123.0 0 NA 1 0 0 186 #> 187 11 0.0 0 NA 0 100.0 6 2 0 187 #> 188 11 1.5 0 NA 0 11.4 5 1 0 188 #> 189 11 3.0 0 NA 0 15.4 5 1 0 189 #> 190 11 6.0 0 NA 0 17.5 5 1 0 190 #> 191 11 12.0 0 NA 0 14.0 5 1 0 191 #> 192 11 24.0 0 NA 0 9.0 5 1 0 192 #> 193 11 24.0 0 NA 0 37.0 6 2 0 193 #> 194 11 36.0 0 NA 0 8.9 5 1 0 194 #> 195 11 36.0 0 NA 0 24.0 6 2 0 195 #> 196 11 48.0 0 NA 0 6.6 5 1 0 196 #> 197 11 48.0 0 NA 0 14.0 6 2 0 197 #> 198 11 72.0 0 NA 0 4.2 5 1 0 198 #> 199 11 72.0 0 NA 0 11.0 6 2 0 199 #> 200 11 96.0 0 NA 0 3.6 5 1 0 200 #> 201 11 96.0 0 NA 0 14.0 6 2 0 201 #> 202 11 120.0 0 NA 0 2.6 5 1 0 202 #> 203 11 120.0 0 NA 0 23.0 6 2 0 203 #> 204 11 144.0 0 NA 0 33.0 6 2 0 204 #> 205 12 0.0 1 113.0 0 NA 1 0 0 205 #> 206 12 0.0 0 NA 0 85.0 6 2 0 206 #> 207 12 1.5 0 NA 0 0.6 5 1 0 207 #> 208 12 3.0 0 NA 0 2.8 5 1 0 208 #> 209 12 6.0 0 NA 0 13.8 5 1 0 209 #> 210 12 9.0 0 NA 0 15.0 5 1 0 210 #> 211 12 24.0 0 NA 0 10.5 5 1 0 211 #> 212 12 24.0 0 NA 0 25.0 6 2 0 212 #> 213 12 36.0 0 NA 0 9.1 5 1 0 213 #> 214 12 36.0 0 NA 0 15.0 6 2 0 214 #> 215 12 48.0 0 NA 0 6.6 5 1 0 215 #> 216 12 48.0 0 NA 0 11.0 6 2 0 216 #> 217 12 72.0 0 NA 0 4.9 5 1 0 217 #> 218 12 96.0 0 NA 0 2.4 5 1 0 218 #> 219 12 120.0 0 NA 0 1.9 5 1 0 219 #> 220 13 0.0 1 113.0 0 NA 1 0 0 220 #> 221 13 0.0 0 NA 0 88.0 6 2 0 221 #> 222 13 1.5 0 NA 0 3.6 5 1 0 222 #> 223 13 3.0 0 NA 0 12.9 5 1 0 223 #> 224 13 6.0 0 NA 0 12.9 5 1 0 224 #> 225 13 9.0 0 NA 0 10.2 5 1 0 225 #> 226 13 24.0 0 NA 0 6.4 5 1 0 226 #> 227 13 24.0 0 NA 0 41.0 6 2 0 227 #> 228 13 36.0 0 NA 0 6.9 5 1 0 228 #> 229 13 36.0 0 NA 0 23.0 6 2 0 229 #> 230 13 48.0 0 NA 0 4.5 5 1 0 230 #> 231 13 48.0 0 NA 0 16.0 6 2 0 231 #> 232 13 72.0 0 NA 0 3.2 5 1 0 232 #> 233 13 72.0 0 NA 0 14.0 6 2 0 233 #> 234 13 96.0 0 NA 0 2.4 5 1 0 234 #> 235 13 96.0 0 NA 0 18.0 6 2 0 235 #> 236 13 120.0 0 NA 0 1.3 5 1 0 236 #> 237 13 120.0 0 NA 0 22.0 6 2 0 237 #> 238 13 144.0 0 NA 0 35.0 6 2 0 238 #> 239 14 0.0 1 75.0 0 NA 1 0 0 239 #> 240 14 0.0 0 NA 0 85.0 6 2 0 240 #> 241 14 0.5 0 NA 0 0.0 5 1 0 241 #> 242 14 1.0 0 NA 0 2.7 5 1 0 242 #> 243 14 2.0 0 NA 0 11.6 5 1 0 243 #> 244 14 3.0 0 NA 0 11.6 5 1 0 244 #> 245 14 6.0 0 NA 0 11.3 5 1 0 245 #> 246 14 9.0 0 NA 0 9.7 5 1 0 246 #> 247 14 24.0 0 NA 0 6.5 5 1 0 247 #> 248 14 24.0 0 NA 0 32.0 6 2 0 248 #> 249 14 36.0 0 NA 0 5.2 5 1 0 249 #> 250 14 36.0 0 NA 0 22.0 6 2 0 250 #> 251 14 48.0 0 NA 0 3.6 5 1 0 251 #> 252 14 48.0 0 NA 0 21.0 6 2 0 252 #> 253 14 72.0 0 NA 0 2.4 5 1 0 253 #> 254 14 72.0 0 NA 0 28.0 6 2 0 254 #> 255 14 96.0 0 NA 0 0.9 5 1 0 255 #> 256 14 96.0 0 NA 0 38.0 6 2 0 256 #> 257 14 120.0 0 NA 0 46.0 6 2 0 257 #> 258 14 144.0 0 NA 0 65.0 6 2 0 258 #> 259 15 0.0 1 85.0 0 NA 1 0 0 259 #> 260 15 0.0 0 NA 0 100.0 6 2 0 260 #> 261 15 1.0 0 NA 0 6.6 5 1 0 261 #> 262 15 3.0 0 NA 0 11.9 5 1 0 262 #> 263 15 6.0 0 NA 0 11.7 5 1 0 263 #> 264 15 9.0 0 NA 0 12.2 5 1 0 264 #> 265 15 24.0 0 NA 0 8.1 5 1 0 265 #> 266 15 24.0 0 NA 0 43.0 6 2 0 266 #> 267 15 36.0 0 NA 0 7.4 5 1 0 267 #> 268 15 36.0 0 NA 0 26.0 6 2 0 268 #> 269 15 48.0 0 NA 0 6.8 5 1 0 269 #> 270 15 48.0 0 NA 0 15.0 6 2 0 270 #> 271 15 72.0 0 NA 0 5.3 5 1 0 271 #> 272 15 72.0 0 NA 0 13.0 6 2 0 272 #> 273 15 96.0 0 NA 0 3.0 5 1 0 273 #> 274 15 96.0 0 NA 0 21.0 6 2 0 274 #> 275 15 120.0 0 NA 0 2.0 5 1 0 275 #> 276 15 120.0 0 NA 0 28.0 6 2 0 276 #> 277 15 144.0 0 NA 0 39.0 6 2 0 277 #> 278 16 0.0 1 87.0 0 NA 1 0 0 278 #> 279 16 0.0 0 NA 0 100.0 6 2 0 279 #> 280 16 24.0 0 NA 0 10.4 5 1 0 280 #> 281 16 24.0 0 NA 0 42.0 6 2 0 281 #> 282 16 36.0 0 NA 0 8.9 5 1 0 282 #> 283 16 36.0 0 NA 0 32.0 6 2 0 283 #> 284 16 48.0 0 NA 0 7.0 5 1 0 284 #> 285 16 48.0 0 NA 0 26.0 6 2 0 285 #> 286 16 72.0 0 NA 0 4.4 5 1 0 286 #> 287 16 72.0 0 NA 0 31.0 6 2 0 287 #> 288 16 96.0 0 NA 0 3.2 5 1 0 288 #> 289 16 96.0 0 NA 0 33.0 6 2 0 289 #> 290 16 120.0 0 NA 0 2.4 5 1 0 290 #> 291 16 120.0 0 NA 0 54.0 6 2 0 291 #> 292 17 0.0 1 117.0 0 NA 1 0 0 292 #> 293 17 0.0 0 NA 0 100.0 6 2 0 293 #> 294 17 24.0 0 NA 0 7.6 5 1 0 294 #> 295 17 24.0 0 NA 0 35.0 6 2 0 295 #> 296 17 36.0 0 NA 0 6.4 5 1 0 296 #> 297 17 36.0 0 NA 0 23.0 6 2 0 297 #> 298 17 48.0 0 NA 0 6.0 5 1 0 298 #> 299 17 48.0 0 NA 0 17.0 6 2 0 299 #> 300 17 72.0 0 NA 0 4.0 5 1 0 300 #> 301 17 72.0 0 NA 0 18.0 6 2 0 301 #> 302 17 96.0 0 NA 0 3.1 5 1 0 302 #> 303 17 96.0 0 NA 0 18.0 6 2 0 303 #> 304 17 120.0 0 NA 0 2.0 5 1 0 304 #> 305 17 120.0 0 NA 0 21.0 6 2 0 305 #> 306 18 0.0 1 112.0 0 NA 1 0 0 306 #> 307 18 0.0 0 NA 0 100.0 6 2 0 307 #> 308 18 24.0 0 NA 0 7.6 5 1 0 308 #> 309 18 24.0 0 NA 0 32.0 6 2 0 309 #> 310 18 36.0 0 NA 0 6.6 5 1 0 310 #> 311 18 36.0 0 NA 0 20.0 6 2 0 311 #> 312 18 48.0 0 NA 0 5.4 5 1 0 312 #> 313 18 48.0 0 NA 0 18.0 6 2 0 313 #> 314 18 72.0 0 NA 0 3.4 5 1 0 314 #> 315 18 72.0 0 NA 0 18.0 6 2 0 315 #> 316 18 96.0 0 NA 0 1.2 5 1 0 316 #> 317 18 96.0 0 NA 0 19.0 6 2 0 317 #> 318 18 120.0 0 NA 0 0.9 5 1 0 318 #> 319 18 120.0 0 NA 0 29.0 6 2 0 319 #> 320 19 0.0 1 95.5 0 NA 1 0 0 320 #> 321 19 0.0 0 NA 0 100.0 6 2 0 321 #> 322 19 24.0 0 NA 0 6.6 5 1 0 322 #> 323 19 24.0 0 NA 0 33.0 6 2 0 323 #> 324 19 36.0 0 NA 0 5.3 5 1 0 324 #> 325 19 36.0 0 NA 0 28.0 6 2 0 325 #> 326 19 48.0 0 NA 0 3.6 5 1 0 326 #> 327 19 48.0 0 NA 0 18.0 6 2 0 327 #> 328 19 72.0 0 NA 0 2.7 5 1 0 328 #> 329 19 72.0 0 NA 0 18.0 6 2 0 329 #> 330 19 96.0 0 NA 0 1.4 5 1 0 330 #> 331 19 96.0 0 NA 0 17.0 6 2 0 331 #> 332 19 120.0 0 NA 0 1.1 5 1 0 332 #> 333 19 120.0 0 NA 0 26.0 6 2 0 333 #> 334 20 0.0 1 88.5 0 NA 1 0 0 334 #> 335 20 0.0 0 NA 0 100.0 6 2 0 335 #> 336 20 24.0 0 NA 0 9.6 5 1 0 336 #> 337 20 24.0 0 NA 0 41.0 6 2 0 337 #> 338 20 36.0 0 NA 0 8.0 5 1 0 338 #> 339 20 36.0 0 NA 0 30.0 6 2 0 339 #> 340 20 48.0 0 NA 0 6.6 5 1 0 340 #> 341 20 48.0 0 NA 0 22.0 6 2 0 341 #> 342 20 72.0 0 NA 0 5.6 5 1 0 342 #> 343 20 72.0 0 NA 0 23.0 6 2 0 343 #> 344 20 96.0 0 NA 0 3.5 5 1 0 344 #> 345 20 96.0 0 NA 0 23.0 6 2 0 345 #> 346 20 120.0 0 NA 0 2.3 5 1 0 346 #> 347 20 120.0 0 NA 0 35.0 6 2 0 347 #> 348 21 0.0 1 93.0 0 NA 1 0 0 348 #> 349 21 0.0 0 NA 0 100.0 6 2 0 349 #> 350 21 24.0 0 NA 0 7.3 5 1 0 350 #> 351 21 24.0 0 NA 0 46.0 6 2 0 351 #> 352 21 36.0 0 NA 0 6.1 5 1 0 352 #> 353 21 36.0 0 NA 0 27.0 6 2 0 353 #> 354 21 48.0 0 NA 0 4.3 5 1 0 354 #> 355 21 48.0 0 NA 0 22.0 6 2 0 355 #> 356 21 72.0 0 NA 0 3.2 5 1 0 356 #> 357 21 72.0 0 NA 0 36.0 6 2 0 357 #> 358 21 96.0 0 NA 0 2.3 5 1 0 358 #> 359 21 96.0 0 NA 0 40.0 6 2 0 359 #> 360 21 120.0 0 NA 0 1.9 5 1 0 360 #> 361 21 120.0 0 NA 0 44.0 6 2 0 361 #> 362 22 0.0 1 87.0 0 NA 1 0 0 362 #> 363 22 0.0 0 NA 0 100.0 6 2 0 363 #> 364 22 24.0 0 NA 0 8.9 5 1 0 364 #> 365 22 24.0 0 NA 0 35.0 6 2 0 365 #> 366 22 36.0 0 NA 0 8.4 5 1 0 366 #> 367 22 36.0 0 NA 0 27.0 6 2 0 367 #> 368 22 48.0 0 NA 0 8.0 5 1 0 368 #> 369 22 48.0 0 NA 0 23.0 6 2 0 369 #> 370 22 72.0 0 NA 0 4.4 5 1 0 370 #> 371 22 72.0 0 NA 0 27.0 6 2 0 371 #> 372 22 96.0 0 NA 0 3.2 5 1 0 372 #> 373 22 96.0 0 NA 0 43.0 6 2 0 373 #> 374 22 120.0 0 NA 0 1.7 5 1 0 374 #> 375 22 120.0 0 NA 0 43.0 6 2 0 375 #> 376 23 0.0 1 110.0 0 NA 1 0 0 376 #> 377 23 0.0 0 NA 0 100.0 6 2 0 377 #> 378 23 24.0 0 NA 0 9.8 5 1 0 378 #> 379 23 24.0 0 NA 0 34.0 6 2 0 379 #> 380 23 36.0 0 NA 0 8.4 5 1 0 380 #> 381 23 36.0 0 NA 0 24.0 6 2 0 381 #> 382 23 48.0 0 NA 0 6.6 5 1 0 382 #> 383 23 48.0 0 NA 0 15.0 6 2 0 383 #> 384 23 72.0 0 NA 0 4.8 5 1 0 384 #> 385 23 72.0 0 NA 0 15.0 6 2 0 385 #> 386 23 96.0 0 NA 0 3.2 5 1 0 386 #> 387 23 96.0 0 NA 0 19.0 6 2 0 387 #> 388 23 120.0 0 NA 0 2.4 5 1 0 388 #> 389 23 120.0 0 NA 0 19.0 6 2 0 389 #> 390 24 0.0 1 115.0 0 NA 1 0 0 390 #> 391 24 0.0 0 NA 0 88.0 6 2 0 391 #> 392 24 24.0 0 NA 0 8.2 5 1 0 392 #> 393 24 24.0 0 NA 0 37.0 6 2 0 393 #> 394 24 36.0 0 NA 0 7.5 5 1 0 394 #> 395 24 36.0 0 NA 0 20.0 6 2 0 395 #> 396 24 48.0 0 NA 0 6.8 5 1 0 396 #> 397 24 48.0 0 NA 0 20.0 6 2 0 397 #> 398 24 72.0 0 NA 0 5.5 5 1 0 398 #> 399 24 72.0 0 NA 0 26.0 6 2 0 399 #> 400 24 96.0 0 NA 0 4.5 5 1 0 400 #> 401 24 96.0 0 NA 0 28.0 6 2 0 401 #> 402 24 120.0 0 NA 0 3.7 5 1 0 402 #> 403 24 120.0 0 NA 0 50.0 6 2 0 403 #> 404 25 0.0 1 112.0 0 NA 1 0 0 404 #> 405 25 0.0 0 NA 0 100.0 6 2 0 405 #> 406 25 24.0 0 NA 0 11.0 5 1 0 406 #> 407 25 24.0 0 NA 0 32.0 6 2 0 407 #> 408 25 36.0 0 NA 0 10.0 5 1 0 408 #> 409 25 36.0 0 NA 0 20.0 6 2 0 409 #> 410 25 48.0 0 NA 0 8.2 5 1 0 410 #> 411 25 48.0 0 NA 0 17.0 6 2 0 411 #> 412 25 72.0 0 NA 0 6.0 5 1 0 412 #> 413 25 72.0 0 NA 0 19.0 6 2 0 413 #> 414 25 96.0 0 NA 0 3.7 5 1 0 414 #> 415 25 96.0 0 NA 0 21.0 6 2 0 415 #> 416 25 120.0 0 NA 0 2.6 5 1 0 416 #> 417 25 120.0 0 NA 0 30.0 6 2 0 417 #> 418 26 0.0 1 120.0 0 NA 1 0 0 418 #> 419 26 0.0 0 NA 0 100.0 6 2 0 419 #> 420 26 24.0 0 NA 0 10.0 5 1 0 420 #> 421 26 24.0 0 NA 0 41.0 6 2 0 421 #> 422 26 36.0 0 NA 0 9.0 5 1 0 422 #> 423 26 36.0 0 NA 0 28.0 6 2 0 423 #> 424 26 48.0 0 NA 0 7.3 5 1 0 424 #> 425 26 48.0 0 NA 0 19.0 6 2 0 425 #> 426 26 72.0 0 NA 0 5.2 5 1 0 426 #> 427 26 72.0 0 NA 0 17.0 6 2 0 427 #> 428 26 96.0 0 NA 0 3.7 5 1 0 428 #> 429 26 96.0 0 NA 0 17.0 6 2 0 429 #> 430 26 120.0 0 NA 0 2.7 5 1 0 430 #> 431 26 120.0 0 NA 0 24.0 6 2 0 431 #> 432 27 0.0 1 120.0 0 NA 1 0 0 432 #> 433 27 0.0 0 NA 0 100.0 6 2 0 433 #> 434 27 24.0 0 NA 0 11.8 5 1 0 434 #> 435 27 24.0 0 NA 0 32.0 6 2 0 435 #> 436 27 36.0 0 NA 0 9.2 5 1 0 436 #> 437 27 36.0 0 NA 0 21.0 6 2 0 437 #> 438 27 48.0 0 NA 0 7.7 5 1 0 438 #> 439 27 48.0 0 NA 0 19.0 6 2 0 439 #> 440 27 72.0 0 NA 0 4.9 5 1 0 440 #> 441 27 72.0 0 NA 0 22.0 6 2 0 441 #> 442 27 96.0 0 NA 0 3.4 5 1 0 442 #> 443 27 96.0 0 NA 0 33.0 6 2 0 443 #> 444 27 120.0 0 NA 0 2.7 5 1 0 444 #> 445 27 120.0 0 NA 0 46.0 6 2 0 445 #> 446 28 0.0 1 120.0 0 NA 1 0 0 446 #> 447 28 0.0 0 NA 0 100.0 6 2 0 447 #> 448 28 24.0 0 NA 0 10.1 5 1 0 448 #> 449 28 24.0 0 NA 0 39.0 6 2 0 449 #> 450 28 36.0 0 NA 0 8.0 5 1 0 450 #> 451 28 36.0 0 NA 0 25.0 6 2 0 451 #> 452 28 48.0 0 NA 0 6.0 5 1 0 452 #> 453 28 48.0 0 NA 0 16.0 6 2 0 453 #> 454 28 72.0 0 NA 0 4.9 5 1 0 454 #> 455 28 72.0 0 NA 0 14.0 6 2 0 455 #> 456 28 96.0 0 NA 0 3.4 5 1 0 456 #> 457 28 96.0 0 NA 0 15.0 6 2 0 457 #> 458 28 120.0 0 NA 0 2.0 5 1 0 458 #> 459 28 120.0 0 NA 0 20.0 6 2 0 459 #> 460 29 0.0 1 153.0 0 NA 1 0 0 460 #> 461 29 0.0 0 NA 0 86.0 6 2 0 461 #> 462 29 24.0 0 NA 0 8.3 5 1 0 462 #> 463 29 24.0 0 NA 0 35.0 6 2 0 463 #> 464 29 36.0 0 NA 0 7.0 5 1 0 464 #> 465 29 36.0 0 NA 0 21.0 6 2 0 465 #> 466 29 48.0 0 NA 0 5.6 5 1 0 466 #> 467 29 48.0 0 NA 0 18.0 6 2 0 467 #> 468 29 72.0 0 NA 0 4.1 5 1 0 468 #> 469 29 72.0 0 NA 0 20.0 6 2 0 469 #> 470 29 96.0 0 NA 0 3.1 5 1 0 470 #> 471 29 96.0 0 NA 0 29.0 6 2 0 471 #> 472 29 120.0 0 NA 0 2.2 5 1 0 472 #> 473 29 120.0 0 NA 0 41.0 6 2 0 473 #> 474 30 0.0 1 105.0 0 NA 1 0 0 474 #> 475 30 0.0 0 NA 0 100.0 6 2 0 475 #> 476 30 24.0 0 NA 0 9.9 5 1 0 476 #> 477 30 24.0 0 NA 0 45.0 6 2 0 477 #> 478 30 36.0 0 NA 0 7.5 5 1 0 478 #> 479 30 36.0 0 NA 0 24.0 6 2 0 479 #> 480 30 48.0 0 NA 0 6.5 5 1 0 480 #> 481 30 48.0 0 NA 0 23.0 6 2 0 481 #> 482 30 72.0 0 NA 0 4.1 5 1 0 482 #> 483 30 72.0 0 NA 0 26.0 6 2 0 483 #> 484 30 96.0 0 NA 0 2.9 5 1 0 484 #> 485 30 96.0 0 NA 0 28.0 6 2 0 485 #> 486 30 120.0 0 NA 0 2.3 5 1 0 486 #> 487 30 120.0 0 NA 0 39.0 6 2 0 487 #> 488 31 0.0 1 125.0 0 NA 1 0 0 488 #> 489 31 0.0 0 NA 0 100.0 6 2 0 489 #> 490 31 24.0 0 NA 0 9.5 5 1 0 490 #> 491 31 24.0 0 NA 0 45.0 6 2 0 491 #> 492 31 36.0 0 NA 0 7.8 5 1 0 492 #> 493 31 36.0 0 NA 0 30.0 6 2 0 493 #> 494 31 48.0 0 NA 0 6.4 5 1 0 494 #> 495 31 48.0 0 NA 0 24.0 6 2 0 495 #> 496 31 72.0 0 NA 0 4.5 5 1 0 496 #> 497 31 72.0 0 NA 0 22.0 6 2 0 497 #> 498 31 96.0 0 NA 0 3.4 5 1 0 498 #> 499 31 96.0 0 NA 0 28.0 6 2 0 499 #> 500 31 120.0 0 NA 0 2.5 5 1 0 500 #> 501 31 120.0 0 NA 0 42.0 6 2 0 501 #> 502 32 0.0 1 93.0 0 NA 1 0 0 502 #> 503 32 0.0 0 NA 0 100.0 6 2 0 503 #> 504 32 24.0 0 NA 0 8.9 5 1 0 504 #> 505 32 24.0 0 NA 0 36.0 6 2 0 505 #> 506 32 36.0 0 NA 0 7.7 5 1 0 506 #> 507 32 36.0 0 NA 0 27.0 6 2 0 507 #> 508 32 48.0 0 NA 0 6.9 5 1 0 508 #> 509 32 48.0 0 NA 0 24.0 6 2 0 509 #> 510 32 72.0 0 NA 0 4.4 5 1 0 510 #> 511 32 72.0 0 NA 0 23.0 6 2 0 511 #> 512 32 96.0 0 NA 0 3.5 5 1 0 512 #> 513 32 96.0 0 NA 0 20.0 6 2 0 513 #> 514 32 120.0 0 NA 0 2.5 5 1 0 514 #> 515 32 120.0 0 NA 0 22.0 6 2 0 515"},{"path":"/reference/getStandardColNames.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine standardized rxode2 column names from data — getStandardColNames","title":"Determine standardized rxode2 column names from data — getStandardColNames","text":"Determine standardized rxode2 column names data","code":""},{"path":"/reference/getStandardColNames.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine standardized rxode2 column names from data — getStandardColNames","text":"","code":"getStandardColNames(data)"},{"path":"/reference/getStandardColNames.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine standardized rxode2 column names from data — getStandardColNames","text":"data data.frame source column names","code":""},{"path":"/reference/getStandardColNames.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine standardized rxode2 column names from data — getStandardColNames","text":"named character vector names standardized names values either name column data NA column present data.","code":""},{"path":"/reference/getStandardColNames.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine standardized rxode2 column names from data — getStandardColNames","text":"","code":"getStandardColNames(data.frame(ID=1, DV=2, Time=3, CmT=4)) #> id time amt rate dur evid cmt ss ii addl dv #> \"ID\" \"Time\" NA NA NA NA \"CmT\" NA NA NA \"DV\" #> mdv dvid cens limit #> NA NA NA NA"},{"path":"/reference/modelUnitConversion.html","id":null,"dir":"Reference","previous_headings":"","what":"Unit conversion for pharmacokinetic models — modelUnitConversion","title":"Unit conversion for pharmacokinetic models — modelUnitConversion","text":"Unit conversion pharmacokinetic models","code":""},{"path":"/reference/modelUnitConversion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unit conversion for pharmacokinetic models — modelUnitConversion","text":"","code":"modelUnitConversion( dvu = NA_character_, amtu = NA_character_, timeu = NA_character_, volumeu = NA_character_ )"},{"path":"/reference/modelUnitConversion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unit conversion for pharmacokinetic models — modelUnitConversion","text":"dvu, amtu, timeu units DV, AMT, TIME columns data volumeu units volume parameters model","code":""},{"path":"/reference/modelUnitConversion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Unit conversion for pharmacokinetic models — modelUnitConversion","text":"list names units associated parameter (\"amtu\", \"clearanceu\", \"volumeu\", \"timeu\", \"dvu\") numeric value multiply modeled estimate (example, cp) model consistent data units.","code":""},{"path":[]},{"path":"/reference/modelUnitConversion.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unit conversion for pharmacokinetic models — modelUnitConversion","text":"","code":"modelUnitConversion(dvu = \"ng/mL\", amtu = \"mg\", timeu = \"hr\", volumeu = \"L\") #> Loading required namespace: testthat #> $amtu #> [1] \"mg\" #> #> $clearanceu #> [1] \"L/h\" #> #> $volumeu #> [1] \"L\" #> #> $timeu #> [1] \"hr\" #> #> $dvu #> [1] \"ng/mL\" #> #> $cmtu #> [1] \"mg/L\" #> #> $dvConversion #> [1] 1000 #>"},{"path":"/reference/monolixControl.html","id":null,"dir":"Reference","previous_headings":"","what":"Monolix Controller for nlmixr2 — monolixControl","title":"Monolix Controller for nlmixr2 — monolixControl","text":"Monolix Controller nlmixr2","code":""},{"path":"/reference/monolixControl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Monolix Controller for nlmixr2 — monolixControl","text":"","code":"monolixControl( nbSSDoses = 7, useLinearization = FALSE, stiff = FALSE, addProp = c(\"combined2\", \"combined1\"), exploratoryAutoStop = FALSE, smoothingAutoStop = FALSE, burnInIterations = 5, smoothingIterations = 200, exploratoryIterations = 250, simulatedAnnealingIterations = 250, exploratoryInterval = 200, exploratoryAlpha = 0, omegaTau = 0.95, errorModelTau = 0.95, variability = c(\"none\", \"firstStage\", \"decreasing\"), runCommand = getOption(\"babelmixr2.monolix\", \"\"), rxControl = NULL, sumProd = FALSE, optExpression = TRUE, calcTables = TRUE, compress = TRUE, ci = 0.95, sigdigTable = NULL, absolutePath = FALSE, modelName = NULL, muRefCovAlg = TRUE, ... )"},{"path":"/reference/monolixControl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Monolix Controller for nlmixr2 — monolixControl","text":"nbSSDoses Number steady state doses (default 7) useLinearization Use linearization log likelihood fim. stiff boolean using stiff ODE solver addProp specifies type additive plus proportional errors, one standard deviations add (combined1) type variances add (combined2). combined1 error type can described following equation: y = f + (+ b*f^c)*err combined2 error model can described following equation: y = f + sqrt(^2 + b^2*(f^c)^2)*err : - y represents observed value - f represents predicted value - additive standard deviation - b proportional/power standard deviation - c power exponent (proportional case c=1) exploratoryAutoStop logical turn exploratory phase auto-stop SAEM (default 250) smoothingAutoStop Boolean indicating smoothing automatically stop (default `FALSE`) burnInIterations Number burn iterations smoothingIterations Number smoothing iterations exploratoryIterations Number iterations exploratory phase (default 250) simulatedAnnealingIterations Number simulating annealing iterations exploratoryInterval Minimum number iterations exploratory phase (default 200) exploratoryAlpha Convergence memory exploratory phase (used `exploratoryAutoStop` `TRUE`) omegaTau Proportional rate variance simulated annealing errorModelTau Proportional rate error model simulated annealing variability describes methodology parameters without variability. : - Fixed throughout (none) - Variability first stage (firstStage) - Decreasing reaches fixed value (decreasing) runCommand shell command function run monolix; can specify default options(\"babelmixr2.monolix\"=\"runMonolix\"). empty 'lixoftConnectors' available, use lixoftConnectors run monolix. See details function usage. rxControl `rxode2` ODE solving options fitting, created `rxControl()` sumProd boolean indicating model change multiplication high precision multiplication sums high precision sums using PreciseSums package. default FALSE. optExpression Optimize rxode2 expression speed calculation. default turned . calcTables boolean determine foceiFit calculate tables. default TRUE compress object compressed items ci Confidence level tables. default 0.95 95% confidence. sigdigTable Significant digits final output table. specified, matches significant digits `sigdig` optimization algorithm. `sigdig` NULL, use 3. absolutePath Boolean indicating absolute path used monolix runs modelName Model name used generate NONMEM output. `NULL` try infer model name (`x` clear). Otherwise use character outputs. muRefCovAlg controls algebraic expressions can mu-referenced treated mu-referenced covariates : 1. Creating internal data-variable `nlmixrMuDerCov#` algebraic mu-referenced expression 2. Change algebraic expression `nlmixrMuDerCov# * mu_cov_theta` 3. Use internal mu-referenced covariate saem 4. optimization completed, replace `model()` old `model()` expression 5. Remove `nlmixrMuDerCov#` nlmix2 output general, covariates accurate since changes system linear compartment model. Therefore, default `TRUE`. ... Ignored parameters","code":""},{"path":"/reference/monolixControl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Monolix Controller for nlmixr2 — monolixControl","text":"monolix control object","code":""},{"path":"/reference/monolixControl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Monolix Controller for nlmixr2 — monolixControl","text":"runCommand given string, called system() command like: runCommand mlxtran. example, runCommand=\"'/path//monolix/mlxbsub2021' -p \" command line used look like following: '/path//monolix/mlxbsub2021' monolix.mlxtran runCommand given function, called FUN(mlxtran, directory, ui) run Monolix. allows run Monolix way may need, long can write R. babelmixr2 wait function return proceeding. runCommand NA, nlmixr() stop writing model files without starting Monolix.","code":""},{"path":"/reference/monolixControl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Monolix Controller for nlmixr2 — monolixControl","text":"Matthew Fidler","code":""},{"path":"/reference/nlmixr2Est.pknca.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate starting parameters using PKNCA — nlmixr2Est.pknca","title":"Estimate starting parameters using PKNCA — nlmixr2Est.pknca","text":"Estimate starting parameters using PKNCA","code":""},{"path":"/reference/nlmixr2Est.pknca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate starting parameters using PKNCA — nlmixr2Est.pknca","text":"","code":"# S3 method for pknca nlmixr2Est(env, ...)"},{"path":"/reference/nlmixr2Est.pknca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate starting parameters using PKNCA — nlmixr2Est.pknca","text":"env Environment nlmixr2 estimation routines. needs : - rxode2 ui object `$ui` - data fit estimation routine `$data` - control estimation routine's control options `$ui` ... arguments provided `nlmixr2Est()` provided flexibility currently used inside nlmixr","code":""},{"path":"/reference/nlmixr2Est.pknca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate starting parameters using PKNCA — nlmixr2Est.pknca","text":"model updated starting parameters. model new element named \"nca\" available includes PKNCA results used calculation.","code":""},{"path":"/reference/nlmixr2Est.pknca.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate starting parameters using PKNCA — nlmixr2Est.pknca","text":"Parameters estimated follows: ka4 half-lives Tmax higher 3: log(2)/(tmax/4) vcInverse dose-normalized Cmax clEstimated median clearance vp,vp22- 4-fold vc, respectively default, controlled vpMult vp2Mult arguments pkncaControl q,q20.5- 0.25-fold cl, respectively default, controlled qMult q2Mult arguments pkncaControl bounds parameter estimates set 10 10 times 99th percentile. (ka, lower bound set lower 10 modified 10 times 99th percentile.) Parameter estimation methods may changed future version.","code":""},{"path":"/reference/nmGetDistributionMonolixLines.html","id":null,"dir":"Reference","previous_headings":"","what":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionMonolixLines","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionMonolixLines","text":"S3 method getting distribution lines base rxode2 saem problem","code":""},{"path":"/reference/nmGetDistributionMonolixLines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionMonolixLines","text":"","code":"nmGetDistributionMonolixLines(line) # S3 method for rxUi nmGetDistributionMonolixLines(line) # S3 method for norm nmGetDistributionMonolixLines(line)"},{"path":"/reference/nmGetDistributionMonolixLines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionMonolixLines","text":"line Parsed rxode2 model environment","code":""},{"path":"/reference/nmGetDistributionMonolixLines.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionMonolixLines","text":"Lines estimation monolix","code":""},{"path":"/reference/nmGetDistributionMonolixLines.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionMonolixLines","text":"Matthew Fidler","code":""},{"path":"/reference/nmGetDistributionNonmemLines.html","id":null,"dir":"Reference","previous_headings":"","what":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionNonmemLines","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionNonmemLines","text":"S3 method getting distribution lines base rxode2 saem problem","code":""},{"path":"/reference/nmGetDistributionNonmemLines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionNonmemLines","text":"","code":"nmGetDistributionNonmemLines(line) # S3 method for rxUi nmGetDistributionNonmemLines(line) # S3 method for norm nmGetDistributionNonmemLines(line)"},{"path":"/reference/nmGetDistributionNonmemLines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionNonmemLines","text":"line Parsed rxode2 model environment","code":""},{"path":"/reference/nmGetDistributionNonmemLines.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionNonmemLines","text":"Lines estimation nonmem","code":""},{"path":"/reference/nmGetDistributionNonmemLines.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionNonmemLines","text":"Matthew Fidler","code":""},{"path":"/reference/nonmemControl.html","id":null,"dir":"Reference","previous_headings":"","what":"NONMEM estimation control — nonmemControl","title":"NONMEM estimation control — nonmemControl","text":"NONMEM estimation control","code":""},{"path":"/reference/nonmemControl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"NONMEM estimation control — nonmemControl","text":"","code":"nonmemControl( est = c(\"focei\", \"imp\", \"its\", \"posthoc\"), advanOde = c(\"advan13\", \"advan8\", \"advan6\"), cov = c(\"r,s\", \"r\", \"s\", \"\"), maxeval = 1e+05, tol = 6, atol = 12, sstol = 6, ssatol = 12, sigl = 12, sigdig = 3, print = 1, extension = getOption(\"babelmixr2.nmModelExtension\", \".nmctl\"), outputExtension = getOption(\"babelmixr2.nmOutputExtension\", \".lst\"), runCommand = getOption(\"babelmixr2.nonmem\", \"\"), iniSigDig = 5, protectZeros = TRUE, muRef = TRUE, addProp = c(\"combined2\", \"combined1\"), rxControl = NULL, sumProd = FALSE, optExpression = TRUE, calcTables = TRUE, compress = TRUE, ci = 0.95, sigdigTable = NULL, readRounding = FALSE, readBadOpt = FALSE, niter = 100L, isample = 1000L, iaccept = 0.4, iscaleMin = 0.1, iscaleMax = 10, df = 4, seed = 14456, mapiter = 1, mapinter = 0, noabort = TRUE, modelName = NULL, muRefCovAlg = TRUE, ... )"},{"path":"/reference/nonmemControl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"NONMEM estimation control — nonmemControl","text":"est NONMEM estimation method advanOde ODE solving method NONMEM cov NONMEM covariance method maxeval NONMEM's maxeval (non posthoc methods) tol NONMEM tolerance ODE solving advan atol NONMEM absolute tolerance ODE solving sstol NONMEM tolerance steady state ODE solving ssatol NONMEM absolute tolerance steady state ODE solving sigl NONMEM sigl estimation option sigdig significant digits NONMEM print print number NONMEM extension NONMEM file extensions outputExtension Extension use NONMEM output listing runCommand Command run NONMEM (typically path \"nmfe75\") function. See details information. iniSigDig many significant digits printed $THETA $OMEGA estimate zero. Also controls zero protection numbers protectZeros Add methods protect divide zero muRef Automatically mu-reference control stream addProp, sumProd, optExpression, calcTables, compress, ci, sigdigTable Passed nlmixr2est::foceiControl rxControl Options pass rxode2::rxControl simulations readRounding Try read NONMEM output NONMEM terminated due rounding errors readBadOpt Try read NONMEM output NONMEM terminated due apparent failed optimization niter number iterations NONMEM estimation methods isample Isample argument NONMEM estimation method iaccept Iaccept NONMEM estimation methods iscaleMin parameter IMP NONMEM method (ISCALE_MIN) iscaleMax parameter IMP NONMEM method (ISCALE_MAX) df degrees freedom IMP method seed seed NONMEM methods mapiter number map iterations IMP method mapinter MAPINTER parameter IMP method noabort Add `NOABORT` option `$EST` modelName Model name used generate NONMEM output. `NULL` try infer model name (`x` clear). Otherwise use character outputs. muRefCovAlg controls algebraic expressions can mu-referenced treated mu-referenced covariates : 1. Creating internal data-variable `nlmixrMuDerCov#` algebraic mu-referenced expression 2. Change algebraic expression `nlmixrMuDerCov# * mu_cov_theta` 3. Use internal mu-referenced covariate saem 4. optimization completed, replace `model()` old `model()` expression 5. Remove `nlmixrMuDerCov#` nlmix2 output general, covariates accurate since changes system linear compartment model. Therefore, default `TRUE`. ... optional genRxControl argument controlling automatic rxControl generation.","code":""},{"path":"/reference/nonmemControl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"NONMEM estimation control — nonmemControl","text":"babelmixr2 control option generating NONMEM control stream reading back `babelmixr2`/`nlmixr2`","code":""},{"path":"/reference/nonmemControl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"NONMEM estimation control — nonmemControl","text":"runCommand given string, called system() command like: runCommand controlFile outputFile. example, runCommand=\"'/path//nmfe75'\" command line used look like following: '/path//nmfe75' one.cmt.nmctl one.cmt.lst runCommand given function, called FUN(ctl, directory, ui) run NONMEM. allows run NONMEM way may need, long can write R. babelmixr2 wait function return proceeding. runCommand NA, nlmixr() stop writing model files without starting NONMEM.","code":""},{"path":"/reference/nonmemControl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"NONMEM estimation control — nonmemControl","text":"Matthew L. Fidler","code":""},{"path":"/reference/nonmemControl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"NONMEM estimation control — nonmemControl","text":"","code":"nonmemControl() #> $est #> [1] \"focei\" #> #> $cov #> [1] \"r,s\" #> #> $advanOde #> [1] \"advan13\" #> #> $maxeval #> [1] 1e+05 #> #> $print #> [1] 1 #> #> $noabort #> [1] TRUE #> #> $iniSigDig #> [1] 5 #> #> $tol #> [1] 6 #> #> $atol #> [1] 12 #> #> $sstol #> [1] 6 #> #> $ssatol #> [1] 12 #> #> $sigl #> [1] 12 #> #> $muRef #> [1] TRUE #> #> $sigdig #> [1] 3 #> #> $protectZeros #> [1] TRUE #> #> $runCommand #> [1] \"\" #> #> $outputExtension #> [1] \".lst\" #> #> $addProp #> [1] \"combined2\" #> #> $rxControl #> $scale #> NULL #> #> $method #> liblsoda #> 2 #> #> $atol #> [1] 1e-12 #> #> $rtol #> [1] 1e-06 #> #> $maxsteps #> [1] 70000 #> #> $hmin #> [1] 0 #> #> $hmax #> [1] NA #> #> $hini #> [1] 0 #> #> $maxordn #> [1] 12 #> #> $maxords #> [1] 5 #> #> $covsInterpolation #> nocb #> 2 #> #> $addCov #> [1] TRUE #> #> $returnType #> rxSolve #> 0 #> #> $sigma #> NULL #> #> $sigmaDf #> NULL #> #> $nCoresRV #> [1] 1 #> #> $sigmaIsChol #> [1] FALSE #> #> $sigmaSeparation #> [1] \"auto\" #> #> $sigmaXform #> identity #> 4 #> #> $nDisplayProgress #> [1] 10000 #> #> $amountUnits #> [1] NA #> #> $timeUnits #> [1] \"hours\" #> #> $addDosing #> [1] FALSE #> #> $stateTrim #> [1] Inf #> #> $updateObject #> [1] FALSE #> #> $omega #> NULL #> #> $omegaDf #> NULL #> #> $omegaIsChol #> [1] FALSE #> #> $omegaSeparation #> [1] \"auto\" #> #> $omegaXform #> variance #> 6 #> #> $nSub #> [1] 1 #> #> $thetaMat #> NULL #> #> $thetaDf #> NULL #> #> $thetaIsChol #> [1] FALSE #> #> $nStud #> [1] 1 #> #> $dfSub #> [1] 0 #> #> $dfObs #> [1] 0 #> #> $seed #> NULL #> #> $nsim #> NULL #> #> $minSS #> [1] 10 #> #> $maxSS #> [1] 1000 #> #> $strictSS #> [1] 1 #> #> $infSSstep #> [1] 12 #> #> $istateReset #> [1] TRUE #> #> $subsetNonmem #> [1] TRUE #> #> $hmaxSd #> [1] 0 #> #> $maxAtolRtolFactor #> [1] 0.1 #> #> $from #> NULL #> #> $to #> NULL #> #> $by #> NULL #> #> $length.out #> NULL #> #> $iCov #> NULL #> #> $keep #> NULL #> #> $keepF #> character(0) #> #> $drop #> NULL #> #> $warnDrop #> [1] TRUE #> #> $omegaLower #> [1] -Inf #> #> $omegaUpper #> [1] Inf #> #> $sigmaLower #> [1] -Inf #> #> $sigmaUpper #> [1] Inf #> #> $thetaLower #> [1] -Inf #> #> $thetaUpper #> [1] Inf #> #> $indLinPhiM #> [1] 0 #> #> $indLinPhiTol #> [1] 1e-07 #> #> $indLinMatExpType #> expokit #> 2 #> #> $indLinMatExpOrder #> [1] 6 #> #> $idFactor #> [1] TRUE #> #> $mxhnil #> [1] 0 #> #> $hmxi #> [1] 0 #> #> $warnIdSort #> [1] TRUE #> #> $ssAtol #> [1] 1e-12 #> #> $ssRtol #> [1] 1e-06 #> #> $safeZero #> [1] 0 #> #> $sumType #> pairwise #> 1 #> #> $prodType #> long double #> 1 #> #> $sensType #> advan #> 4 #> #> $linDiff #> tlag f rate dur tlag2 f2 rate2 dur2 #> 1.5e-05 1.5e-05 1.5e-05 1.5e-05 1.5e-05 1.5e-05 1.5e-05 1.5e-05 #> #> $linDiffCentral #> tlag f rate dur tlag2 f2 rate2 dur2 #> TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE #> #> $resample #> NULL #> #> $resampleID #> [1] TRUE #> #> $maxwhile #> [1] 100000 #> #> $cores #> [1] 0 #> #> $atolSens #> [1] 1e-08 #> #> $rtolSens #> [1] 1e-06 #> #> $ssAtolSens #> [1] 1e-08 #> #> $ssRtolSens #> [1] 1e-06 #> #> $simVariability #> [1] NA #> #> $nLlikAlloc #> NULL #> #> $useStdPow #> [1] 0 #> #> $naTimeHandle #> ignore #> 1 #> #> $addlKeepsCov #> [1] FALSE #> #> $addlDropSs #> [1] TRUE #> #> $ssAtDoseTime #> [1] TRUE #> #> $ss2cancelAllPending #> [1] FALSE #> #> $.zeros #> NULL #> #> attr(,\"class\") #> [1] \"rxControl\" #> #> $sumProd #> [1] FALSE #> #> $optExpression #> [1] TRUE #> #> $calcTables #> [1] TRUE #> #> $compress #> [1] TRUE #> #> $ci #> [1] 0.95 #> #> $sigdigTable #> NULL #> #> $readRounding #> [1] FALSE #> #> $readBadOpt #> [1] FALSE #> #> $genRxControl #> [1] TRUE #> #> $niter #> [1] 100 #> #> $isample #> [1] 1000 #> #> $iaccept #> [1] 0.4 #> #> $iscaleMin #> [1] 0.1 #> #> $iscaleMax #> [1] 10 #> #> $df #> [1] 4 #> #> $seed #> [1] 14456 #> #> $mapiter #> [1] 1 #> #> $modelName #> NULL #> #> $muRefCovAlg #> [1] TRUE #> #> attr(,\"class\") #> [1] \"nonmemControl\""},{"path":"/reference/pkncaControl.html","id":null,"dir":"Reference","previous_headings":"","what":"PKNCA estimation control — pkncaControl","title":"PKNCA estimation control — pkncaControl","text":"PKNCA estimation control","code":""},{"path":"/reference/pkncaControl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PKNCA estimation control — pkncaControl","text":"","code":"pkncaControl( concu = NA_character_, doseu = NA_character_, timeu = NA_character_, volumeu = NA_character_, vpMult = 2, qMult = 1/2, vp2Mult = 4, q2Mult = 1/4, dvParam = \"cp\", groups = character(), sparse = FALSE, ncaData = NULL, ncaResults = NULL, rxControl = rxode2::rxControl() )"},{"path":"/reference/pkncaControl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PKNCA estimation control — pkncaControl","text":"concu, doseu, timeu concentration, dose, time units source data (passed PKNCA::pknca_units_table()). volumeu compartment volume model (NULL, simplified units source data used) vpMult, qMult, vp2Mult, q2Mult Multipliers vc cl provide initial estimates vp, q, vp2, q2 dvParam parameter name model modified concentration unit conversions. must assigned line , separate residual error model line. groups Grouping columns NCA summaries group (required sparse = TRUE) sparse concentration-time data sparse PK (commonly used small nonclinical species terminal difficult sampling) dense PK (commonly used clinical studies larger nonclinical species)? ncaData Data use calculating NCA parameters. Typical use subset original data informative NCA. ncaResults Already computed NCA results (PKNCAresults object) bypass automatic calculations. least following parameters must calculated NCA: tmax, cmax.dn, cl.last rxControl Control options sent `rxode2::rxControl()`","code":""},{"path":"/reference/pkncaControl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PKNCA estimation control — pkncaControl","text":"list parameters","code":""},{"path":"/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. nlmixr2est getValidNlmixrCtl, nlmixr2Est, nmObjGetControl, nmObjGetFoceiControl, nmObjHandleControlObject nonmem2rx .nonmem2rx, nonmem2rx rxode2 .minfo, rxModelVars, rxUiGet","code":""},{"path":"/reference/rxToMonolix.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert RxODE syntax to monolix syntax — rxToMonolix","title":"Convert RxODE syntax to monolix syntax — rxToMonolix","text":"Convert RxODE syntax monolix syntax","code":""},{"path":"/reference/rxToMonolix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert RxODE syntax to monolix syntax — rxToMonolix","text":"","code":"rxToMonolix(x, ui)"},{"path":"/reference/rxToMonolix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert RxODE syntax to monolix syntax — rxToMonolix","text":"x Expression ui rxode2 ui","code":""},{"path":"/reference/rxToMonolix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert RxODE syntax to monolix syntax — rxToMonolix","text":"Monolix syntax","code":""},{"path":"/reference/rxToMonolix.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convert RxODE syntax to monolix syntax — rxToMonolix","text":"Matthew Fidler","code":""},{"path":"/reference/rxToNonmem.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert RxODE syntax to NONMEM syntax — rxToNonmem","title":"Convert RxODE syntax to NONMEM syntax — rxToNonmem","text":"Convert RxODE syntax NONMEM syntax","code":""},{"path":"/reference/rxToNonmem.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert RxODE syntax to NONMEM syntax — rxToNonmem","text":"","code":"rxToNonmem(x, ui)"},{"path":"/reference/rxToNonmem.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert RxODE syntax to NONMEM syntax — rxToNonmem","text":"x Expression ui rxode2 ui","code":""},{"path":"/reference/rxToNonmem.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert RxODE syntax to NONMEM syntax — rxToNonmem","text":"NONMEM syntax","code":""},{"path":"/reference/rxToNonmem.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convert RxODE syntax to NONMEM syntax — rxToNonmem","text":"Matthew Fidler","code":""},{"path":"/reference/simplifyUnit.html","id":null,"dir":"Reference","previous_headings":"","what":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","title":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","text":"Simplify units removing repeated units numerator denominator","code":""},{"path":"/reference/simplifyUnit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","text":"","code":"simplifyUnit(numerator = \"\", denominator = \"\")"},{"path":"/reference/simplifyUnit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","text":"numerator numerator units (whole unit specification) denominator denominator units (NULL numerator whole unit specification)","code":""},{"path":"/reference/simplifyUnit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","text":"units specified units numerator denominator cancelled.","code":""},{"path":"/reference/simplifyUnit.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","text":"NA \"\" numerator denominator considered unitless.","code":""},{"path":[]},{"path":"/reference/simplifyUnit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","text":"","code":"simplifyUnit(\"kg\", \"kg/mL\") #> [1] \"mL\" # units that don't match exactly are not cancelled simplifyUnit(\"kg\", \"g/mL\") #> [1] \"kg*mL/g\""},{"path":"/news/index.html","id":"babelmixr2-development-version","dir":"Changelog","previous_headings":"","what":"babelmixr2 (development version)","title":"babelmixr2 (development version)","text":"Handle algebraic mu expressions PKNCA controller now contains rxControl since used translation options revision load pruned ui model query compartment properties (.e. bioavailability, lag time, etc) writing NONMEM model. fix issues PK block define variables larger calculated variable can used model instead. nonmem2rx different lst file, long nonmem2rx::nminfo(file) works, successful conversion nlmixr2 fit object occur. Fix save parameter history $parHistData accommodate changes focei’s output ($parHist now derived). Changed solving options match new steady state options rxode2 NONMEM implements . Also changed iwres model account rxerr. instead err. updated rxode2 well.","code":""},{"path":"/news/index.html","id":"babelmixr2-011","dir":"Changelog","previous_headings":"","what":"babelmixr2 0.1.1","title":"babelmixr2 0.1.1","text":"CRAN release: 2023-05-27 Add new method .nlmixr2 convert nonmem2rx methods nlmixr fits Dropped pmxTools favor nonmem2rx conserve methods","code":""},{"path":"/news/index.html","id":"babelmixr2-010","dir":"Changelog","previous_headings":"","what":"babelmixr2 0.1.0","title":"babelmixr2 0.1.0","text":"CRAN release: 2022-10-28 Babelmixr support “monolix”, “nonmem”, “pknca” methods release. Added NEWS.md file track changes package.","code":""}]