pmxploit

The goal of pmxploit is to facilitate the post-processing analysis of non-linear mixed effects models developed with NONMEM software.

It provides helper functions for visualizing and summarizing population analyses results, detecting outliers, computing numerical quality criteria and comparing multiple run results.

Requirements

pmxploit can read runs executed with NONMEM 7.2 and higher (with results data contained in an xml file).

Installation

# Install the latest version from GitHub
# install.packages('devtools')
devtools::install_github('pnolain/pmxploit')

Getting started

library(pmxploit)

# load a NONMEM run from its location
run <- load_nm_run("~/pmxploit/example")

# or from an archive file
# run <- load_nm_run("~/pmxploit/example.tar.gz")

Diagnostic plots

plot_dv_vs_predictions(run, compartment = 2L, predictions = c("PRED", "IPRED"),
                       x_scale = "log", y_scale = "log")

plot_residuals(run, compartment = 2L, residuals = "CWRES", idv = c("TIME", "PRED"))

run %>%
  group_by(CMT) %>%
  plot_residuals(compartment = c(2L, 3L), residuals = "CWRES", idv = c("TIME", "PRED"))

Parameters

plot_convergence(run, parameters = "theta")

run %>% 
  summarize_thetas()
#> # A tibble: 13 x 7
#>    id      name  estimate       se      rse  ci_low   ci_up
#>    <chr>   <chr>    <dbl>    <dbl>    <dbl>   <dbl>   <dbl>
#>  1 THETA1  TVCL    0.172   0.0152   0.0884   0.141   0.202 
#>  2 THETA2  TKON  559      NA       NA       NA      NA     
#>  3 THETA3  TKIN    0.124   0.00222  0.0180   0.119   0.128 
#>  4 THETA4  TKDE    1.35    0.0375   0.0279   1.27    1.42  
#>  5 THETA5  TVQ     0.500   0.0286   0.0571   0.443   0.558 
#>  6 THETA6  TVV1    3.23    0.204    0.0632   2.82    3.63  
#>  7 THETA7  TVV2    2.61   NA       NA       NA      NA     
#>  8 THETA8  TVKA    0.638   0.0468   0.0733   0.544   0.731 
#>  9 THETA9  TVF     0.590   0.0239   0.0406   0.542   0.638 
#> 10 THETA10 TVEP    0.275   0.00151  0.00548  0.272   0.278 
#> 11 THETA11 TVEA    0.582   0.0224   0.0385   0.537   0.627 
#> 12 THETA12 TVLAG   0.0298  0.00110  0.0369   0.0276  0.0320
#> 13 THETA13 COV1    1.56    0.0851   0.0544   1.39    1.73

run %>% 
  summarize_omega()
#> # A tibble: 7 x 8
#>   eta1  eta2  estimate      se    rse ci_low  ci_up    cv
#>   <chr> <chr>    <dbl>   <dbl>  <dbl>  <dbl>  <dbl> <dbl>
#> 1 ETCL  ETCL    0.279  0.0761  0.273  0.130  0.428  0.568
#> 2 EKIN  EKIN    0.0581 0.00462 0.0796 0.0490 0.0672 0.245
#> 3 EKDE  EKDE    0.171  0.0164  0.0963 0.138  0.203  0.431
#> 4 ETQ   ETQ     0.0691 0.0298  0.431  0.0108 0.127  0.267
#> 5 ETV1  ETV1    0.0990 0.0166  0.167  0.0665 0.132  0.323
#> 6 ETKA  ETKA    0.464  0.0495  0.107  0.367  0.561  0.769
#> 7 ETF   ETF     0.290  0.0717  0.248  0.149  0.430  0.580

run %>% 
  summarize_shrinkage(type = "ETA")
#> # A tibble: 9 x 3
#>   type  parameter shrinkage
#>   <chr> <chr>         <dbl>
#> 1 ETA   ETCL          0.626
#> 2 ETA   EKON          1    
#> 3 ETA   EKIN          0.204
#> 4 ETA   EKDE          0.265
#> 5 ETA   ETQ           0.755
#> 6 ETA   ETV1          0.327
#> 7 ETA   ETV2          1    
#> 8 ETA   ETKA          0.477
#> 9 ETA   ETF           0.438