From 261fa7dec4226bf30f7a7dcd726a29f8d7d1523d Mon Sep 17 00:00:00 2001 From: jreps Date: Wed, 2 Oct 2024 18:18:02 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20OHDSI/Pa?= =?UTF-8?q?tientLevelPrediction@5b8260d03858285dfe42077428a3c6bca1818dbb?= =?UTF-8?q?=20=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- 404.html | 18 +- articles/AddingCustomFeatureEngineering.html | 28 +- articles/AddingCustomModels.html | 44 +- articles/AddingCustomSamples.html | 28 +- articles/AddingCustomSplitting.html | 28 +- articles/BenchmarkTasks.html | 25 +- articles/BestPractices.html | 25 +- .../BuildingMultiplePredictiveModels.html | 28 +- articles/BuildingPredictiveModels.html | 33 +- articles/ClinicalModels.html | 25 +- articles/ConstrainedPredictors.html | 29 +- articles/CreatingLearningCurves.html | 28 +- articles/CreatingNetworkStudies.html | 25 +- articles/InstallationGuide.html | 28 +- articles/Videos.html | 25 +- 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reference/insertModelDesignInDatabase.html | 34 +- reference/insertResultsToSqlite.html | 34 +- reference/listAppend.html | 26 +- reference/listCartesian.html | 28 +- reference/loadPlpAnalysesJson.html | 30 +- reference/loadPlpData.html | 30 +- reference/loadPlpModel.html | 24 +- reference/loadPlpResult.html | 24 +- reference/loadPlpShareable.html | 24 +- reference/loadPrediction.html | 24 +- reference/migrateDataModel.html | 28 +- reference/modelBasedConcordance.html | 32 +- reference/negativeLikelihoodRatio.html | 34 +- reference/negativePredictiveValue.html | 34 +- reference/outcomeSurvivalPlot.html | 38 +- reference/pfi.html | 42 +- reference/plotDemographicSummary.html | 34 +- reference/plotF1Measure.html | 34 +- reference/plotGeneralizability.html | 32 +- reference/plotLearningCurve.html | 44 +- reference/plotPlp.html | 34 +- reference/plotPrecisionRecall.html | 34 +- reference/plotPredictedPDF.html | 34 +- reference/plotPredictionDistribution.html | 34 +- reference/plotPreferencePDF.html | 39 +- reference/plotSmoothCalibration.html | 50 +- reference/plotSparseCalibration.html | 34 +- reference/plotSparseCalibration2.html | 34 +- reference/plotSparseRoc.html | 34 +- reference/plotVariableScatterplot.html | 32 +- reference/plpDataSimulationProfile.html | 20 +- reference/positiveLikelihoodRatio.html | 34 +- reference/positivePredictiveValue.html | 34 +- reference/predictCyclops.html | 32 +- reference/predictPlp.html | 34 +- reference/preprocessData.html | 37 +- reference/recalibratePlp.html | 34 +- reference/recalibratePlpRefit.html | 32 +- reference/runMultiplePlp.html | 40 +- reference/runPlp.html | 69 +- reference/savePlpAnalysesJson.html | 32 +- reference/savePlpData.html | 30 +- reference/savePlpModel.html | 26 +- reference/savePlpResult.html | 26 +- reference/savePlpShareable.html | 28 +- reference/savePrediction.html | 28 +- reference/sensitivity.html | 34 +- reference/setAdaBoost.html | 34 +- reference/setCoxModel.html | 40 +- reference/setDecisionTree.html | 48 +- reference/setGradientBoostingMachine.html | 42 +- reference/setIterativeHardThresholding.html | 44 +- reference/setKNN.html | 32 +- reference/setLassoLogisticRegression.html | 44 +- reference/setLightGBM.html | 46 +- reference/setMLP.html | 68 +- reference/setNaiveBayes.html | 24 +- reference/setPythonEnvironment.html | 26 +- reference/setRandomForest.html | 56 +- reference/setSVM.html | 46 +- reference/simulatePlpData.html | 30 +- reference/sklearnFromJson.html | 24 +- reference/sklearnToJson.html | 26 +- reference/specificity.html | 34 +- reference/splitData.html | 34 +- reference/toSparseM.html | 32 +- reference/validateExternal.html | 30 +- reference/validateMultiplePlp.html | 34 +- reference/viewDatabaseResultPlp.html | 36 +- reference/viewMultiplePlp.html | 24 +- reference/viewPlp.html | 32 +- sitemap.xml | 608 +++++++++++++----- 314 files changed, 4850 insertions(+), 3158 deletions(-) create mode 100644 articles/sparseRoc.webp create mode 100644 dev/articles/ClinicalModels.html diff --git a/404.html b/404.html index af71f8ca6..7dbf2ed28 100644 --- a/404.html +++ b/404.html @@ -6,7 +6,7 @@ Page not found (404) • PatientLevelPrediction - + @@ -18,7 +18,7 @@ - +
- +
@@ -169,16 +169,16 @@

Page not found (404)

-

Site built with pkgdown 2.1.0.

+

Site built with pkgdown 2.0.7.

+ - - + diff --git a/articles/AddingCustomFeatureEngineering.html b/articles/AddingCustomFeatureEngineering.html index bb6212025..b7261a0cc 100644 --- a/articles/AddingCustomFeatureEngineering.html +++ b/articles/AddingCustomFeatureEngineering.html @@ -6,19 +6,20 @@ Adding Custom Feature Engineering Functions • PatientLevelPrediction - + + - +
- +
@@ -152,7 +153,7 @@

Jenna Reps,

2024-09-09

- Source: vignettes/AddingCustomFeatureEngineering.Rmd + Source: vignettes/AddingCustomFeatureEngineering.Rmd
@@ -329,7 +330,8 @@

AcknowledgmentsPatientLevelPrediction package.

 citation("PatientLevelPrediction")
-
## To cite PatientLevelPrediction in publications use:
+
## 
+## To cite PatientLevelPrediction in publications use:
 ## 
 ##   Reps JM, Schuemie MJ, Suchard MA, Ryan PB, Rijnbeek P (2018). "Design
 ##   and implementation of a standardized framework to generate and
@@ -364,7 +366,9 @@ 
Acknowledgments
 
-

+ +
@@ -377,16 +381,16 @@

Acknowledgments

-

Site built with pkgdown 2.1.0.

+

Site built with pkgdown 2.0.7.

+ - - + diff --git a/articles/AddingCustomModels.html b/articles/AddingCustomModels.html index 8d66c8871..3cafc9a9e 100644 --- a/articles/AddingCustomModels.html +++ b/articles/AddingCustomModels.html @@ -6,19 +6,20 @@ Adding Custom Patient-Level Prediction Algorithms • PatientLevelPrediction - + + - +
- +
@@ -153,7 +154,7 @@

Jenna Reps,

2024-09-09

- Source: vignettes/AddingCustomModels.Rmd + Source: vignettes/AddingCustomModels.Rmd
@@ -265,11 +266,11 @@

Fit applying the model
  • featureEngineering - the feature engineering settings e.g., -attr(trainData$covariateData, -"metaData")$featureEngineering,
    • +attr(trainData\(covariateData, +"metaData")\)featureEngineering,
  • tidyCovariates - the preprocessing settings e.g., -attr(trainData$covariateData, -"metaData")$tidyCovariateDataSettings,
    • +attr(trainData\(covariateData, +"metaData")\)tidyCovariateDataSettings,
  • requireDenseMatrix - does the model require a dense matrix? e.g., attr(param, ‘settings’)$requiresDenseMatrix,
@@ -285,11 +286,11 @@

Fit
  • populationSettings - the population settings e.g., attr(trainData, “metaData”)$populationSettings,
  • featureEngineeringSettings - the feature engineering settings e.g., -attr(trainData$covariateData, -"metaData")$featureEngineeringSettings,
    • +attr(trainData\(covariateData, +"metaData")\)featureEngineeringSettings,
  • preprocessSettings - the preprocessing settings e.g., -attr(trainData$covariateData, -"metaData")$preprocessSettings,
    • +attr(trainData\(covariateData, +"metaData")\)preprocessSettings,
  • modelSettings = a list containing: model (model name), param (the hyper-parameter search list), finalModelParameters (the final model hyper-parameters), extraSettings (any extra settings)
    • @@ -713,7 +714,8 @@

    AcknowledgmentsPatientLevelPrediction package.

     citation("PatientLevelPrediction")
    -
    ## To cite PatientLevelPrediction in publications use:
+
    ## 
+## To cite PatientLevelPrediction in publications use:
 ## 
 ##   Reps JM, Schuemie MJ, Suchard MA, Ryan PB, Rijnbeek P (2018). "Design
 ##   and implementation of a standardized framework to generate and
@@ -748,7 +750,9 @@ 
    Acknowledgments
 
-

    + +
    @@ -761,16 +765,16 @@

    Acknowledgments

    -

    Site built with pkgdown 2.1.0.

    +

    Site built with pkgdown 2.0.7.

    + - - + diff --git a/articles/AddingCustomSamples.html b/articles/AddingCustomSamples.html index cc0446d55..84ccdde27 100644 --- a/articles/AddingCustomSamples.html +++ b/articles/AddingCustomSamples.html @@ -6,19 +6,20 @@ Adding Custom Sampling Functions • PatientLevelPrediction - + + - +
    - +
    @@ -151,7 +152,7 @@

    Jenna Reps

    2024-09-09

    - Source: vignettes/AddingCustomSamples.Rmd + Source: vignettes/AddingCustomSamples.Rmd
    @@ -300,7 +301,8 @@

    AcknowledgmentsPatientLevelPrediction package.

     citation("PatientLevelPrediction")
    -
    ## To cite PatientLevelPrediction in publications use:
+
    ## 
+## To cite PatientLevelPrediction in publications use:
 ## 
 ##   Reps JM, Schuemie MJ, Suchard MA, Ryan PB, Rijnbeek P (2018). "Design
 ##   and implementation of a standardized framework to generate and
@@ -335,7 +337,9 @@ 
    Acknowledgments
 
-

    + +
    @@ -348,16 +352,16 @@

    Acknowledgments

    -

    Site built with pkgdown 2.1.0.

    +

    Site built with pkgdown 2.0.7.

    + - - + diff --git a/articles/AddingCustomSplitting.html b/articles/AddingCustomSplitting.html index fae05090c..6f56760db 100644 --- a/articles/AddingCustomSplitting.html +++ b/articles/AddingCustomSplitting.html @@ -6,19 +6,20 @@ Adding Custom Data Splitting Functions • PatientLevelPrediction - + + - +
    - +
    @@ -151,7 +152,7 @@

    Jenna Reps

    2024-09-09

    - Source: vignettes/AddingCustomSplitting.Rmd + Source: vignettes/AddingCustomSplitting.Rmd
    @@ -266,7 +267,8 @@

    AcknowledgmentsPatientLevelPrediction package.

     citation("PatientLevelPrediction")
    -
    ## To cite PatientLevelPrediction in publications use:
+
    ## 
+## To cite PatientLevelPrediction in publications use:
 ## 
 ##   Reps JM, Schuemie MJ, Suchard MA, Ryan PB, Rijnbeek P (2018). "Design
 ##   and implementation of a standardized framework to generate and
@@ -301,7 +303,9 @@ 
    Acknowledgments
 
-

    + +
    @@ -314,16 +318,16 @@

    Acknowledgments

    -

    Site built with pkgdown 2.1.0.

    +

    Site built with pkgdown 2.0.7.

    + - - + diff --git a/articles/BenchmarkTasks.html b/articles/BenchmarkTasks.html index 28509aa30..423651309 100644 --- a/articles/BenchmarkTasks.html +++ b/articles/BenchmarkTasks.html @@ -6,19 +6,20 @@ Benchmark Tasks • PatientLevelPrediction - + + - +
    - +
    @@ -152,7 +153,7 @@

    Jenna Reps,

    2024-09-09

    - Source: vignettes/BenchmarkTasks.Rmd + Source: vignettes/BenchmarkTasks.Rmd
    @@ -312,7 +313,9 @@

    Benchmark Tasks For + +

    @@ -325,16 +328,16 @@

    Benchmark Tasks For

    -

    Site built with pkgdown 2.1.0.

    +

    Site built with pkgdown 2.0.7.

    + - - + diff --git a/articles/BestPractices.html b/articles/BestPractices.html index 4402a97cc..49fc4e02f 100644 --- a/articles/BestPractices.html +++ b/articles/BestPractices.html @@ -6,19 +6,20 @@ Best Practice Research • PatientLevelPrediction - + + - +
    - +
    @@ -152,7 +153,7 @@

    Jenna Reps,

    2024-09-09

    - Source: vignettes/BestPractices.rmd + Source: vignettes/BestPractices.rmd
    @@ -422,7 +423,9 @@

    -

    + +
    @@ -435,16 +438,16 @@

    -

    Site built with pkgdown 2.1.0.

    +

    Site built with pkgdown 2.0.7.

    + - - + diff --git a/articles/BuildingMultiplePredictiveModels.html b/articles/BuildingMultiplePredictiveModels.html index a749721bb..45d404d03 100644 --- a/articles/BuildingMultiplePredictiveModels.html +++ b/articles/BuildingMultiplePredictiveModels.html @@ -6,19 +6,20 @@ Automatically Build Multiple Patient-Level Predictive Models • PatientLevelPrediction - + + - +
    - +
    @@ -153,7 +154,7 @@

    Jenna Reps,

    2024-09-09

    - Source: vignettes/BuildingMultiplePredictiveModels.Rmd + Source: vignettes/BuildingMultiplePredictiveModels.Rmd
    @@ -502,7 +503,8 @@

    AcknowledgmentsPatientLevelPrediction package.

     citation("PatientLevelPrediction")
    -
    ## To cite PatientLevelPrediction in publications use:
+
    ## 
+## To cite PatientLevelPrediction in publications use:
 ## 
 ##   Reps JM, Schuemie MJ, Suchard MA, Ryan PB, Rijnbeek P (2018). "Design
 ##   and implementation of a standardized framework to generate and
@@ -535,7 +537,9 @@ 
    Acknowledgments
 
-

    + +
    @@ -548,16 +552,16 @@

    Acknowledgments

    -

    Site built with pkgdown 2.1.0.

    +

    Site built with pkgdown 2.0.7.

    + - - + diff --git a/articles/BuildingPredictiveModels.html b/articles/BuildingPredictiveModels.html index 8b1be2dc1..4e13c99f2 100644 --- a/articles/BuildingPredictiveModels.html +++ b/articles/BuildingPredictiveModels.html @@ -6,19 +6,20 @@ Building patient-level predictive models • PatientLevelPrediction - + + - +
    - +
    @@ -152,7 +153,7 @@

    Jenna Reps,

    2024-09-09

    - Source: vignettes/BuildingPredictiveModels.Rmd + Source: vignettes/BuildingPredictiveModels.Rmd
    @@ -1871,7 +1872,7 @@

    Discrimination
    -Receiver Operating Characteristic Plot
    Receiver Operating Characteristic Plot
    +Receiver Operating Characteristic Plot
    Receiver Operating Characteristic Plot
    @@ -2134,7 +2135,8 @@

    AcknowledgmentsPatientLevelPrediction package.

     citation("PatientLevelPrediction")
    -
    ## To cite PatientLevelPrediction in publications use:
+
    ## 
+## To cite PatientLevelPrediction in publications use:
 ## 
 ##   Reps JM, Schuemie MJ, Suchard MA, Ryan PB, Rijnbeek P (2018). "Design
 ##   and implementation of a standardized framework to generate and
@@ -2159,7 +2161,8 @@ 
    AcknowledgmentsCyclops package.
    
 
     citation("Cyclops")
    
-
    ## To cite Cyclops in publications use:
+
    ## 
+## To cite Cyclops in publications use:
 ## 
 ##   Suchard MA, Simpson SE, Zorych I, Ryan P, Madigan D (2013). "Massive
 ##   parallelization of serial inference algorithms for complex
@@ -2263,7 +2266,9 @@ 
    Appendi
 
   
+        
+

    @@ -2276,16 +2281,16 @@

    Appendi

    -

    Site built with pkgdown 2.1.0.

    +

    Site built with pkgdown 2.0.7.

    + - - + diff --git a/articles/ClinicalModels.html b/articles/ClinicalModels.html index 579147693..d3cd57891 100644 --- a/articles/ClinicalModels.html +++ b/articles/ClinicalModels.html @@ -6,19 +6,20 @@ Clinical Models • PatientLevelPrediction - + + - +
    - +
    @@ -152,7 +153,7 @@

    Jenna Reps,

    2024-09-09

    - Source: vignettes/ClinicalModels.rmd + Source: vignettes/ClinicalModels.rmd
    @@ -242,7 +243,9 @@

    -

    + +
    @@ -255,16 +258,16 @@

    -

    Site built with pkgdown 2.1.0.

    +

    Site built with pkgdown 2.0.7.

    + - - + diff --git a/articles/ConstrainedPredictors.html b/articles/ConstrainedPredictors.html index 837236b9f..24fa22693 100644 --- a/articles/ConstrainedPredictors.html +++ b/articles/ConstrainedPredictors.html @@ -6,19 +6,20 @@ Constrained predictors • PatientLevelPrediction - + + - +
    - +
    @@ -151,7 +152,7 @@

    Jenna Reps

    2024-09-09

    - Source: vignettes/ConstrainedPredictors.Rmd + Source: vignettes/ConstrainedPredictors.Rmd
    @@ -177,10 +178,10 @@

    How to use the PhenotypeLibra

    To extract the cohort definition for Alcoholism with an id of 1165, just run:

    -PhenotypeLibrary::getPlCohortDefinitionSet(1165)
    +PhenotypeLibrary::getPlCohortDefinitionSet(1165)

    in general you can extract all the cohorts by running:

    -phenotypeDefinitions <- PhenotypeLibrary::getPlCohortDefinitionSet(1152:1215)
    +phenotypeDefinitions <- PhenotypeLibrary::getPlCohortDefinitionSet(1152:1215)

    The full set of predictor phenotypes @@ -530,7 +531,9 @@

    The full set of predictor phenotyp + +

    @@ -543,16 +546,16 @@

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    Site built with pkgdown 2.1.0.

    +

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    + - - + diff --git a/articles/CreatingLearningCurves.html b/articles/CreatingLearningCurves.html index cfaac4ea4..2f44ca17f 100644 --- a/articles/CreatingLearningCurves.html +++ b/articles/CreatingLearningCurves.html @@ -6,19 +6,20 @@ Creating Learning Curves • PatientLevelPrediction - + + - +
    - +
    @@ -152,7 +153,7 @@

    Luis H. John,

    2024-09-09

    - Source: vignettes/CreatingLearningCurves.Rmd + Source: vignettes/CreatingLearningCurves.Rmd
    @@ -368,7 +369,8 @@

    AcknowledgmentsPatientLevelPrediction package.

     citation("PatientLevelPrediction")
    -
    ## To cite PatientLevelPrediction in publications use:
+
    ## 
+## To cite PatientLevelPrediction in publications use:
 ## 
 ##   Reps JM, Schuemie MJ, Suchard MA, Ryan PB, Rijnbeek P (2018). "Design
 ##   and implementation of a standardized framework to generate and
@@ -401,7 +403,9 @@ 
    Acknowledgments
 
-

    + +
    @@ -414,16 +418,16 @@

    Acknowledgments

    -

    Site built with pkgdown 2.1.0.

    +

    Site built with pkgdown 2.0.7.

    + - - + diff --git a/articles/CreatingNetworkStudies.html b/articles/CreatingNetworkStudies.html index f1bcf0e27..bcf3648fa 100644 --- a/articles/CreatingNetworkStudies.html +++ b/articles/CreatingNetworkStudies.html @@ -6,19 +6,20 @@ Making patient-level predictive network study packages • PatientLevelPrediction - + + - +
    - +
    @@ -153,7 +154,7 @@

    Jenna Reps,

    2024-09-09

    - Source: vignettes/CreatingNetworkStudies.Rmd + Source: vignettes/CreatingNetworkStudies.Rmd
    @@ -269,7 +270,9 @@

    Package Skeleton - File Structure

    -

    Site built with pkgdown 2.1.0.

    +

    Site built with pkgdown 2.0.7.

    + - - + diff --git a/articles/InstallationGuide.html b/articles/InstallationGuide.html index 70cdaddc9..054ce33de 100644 --- a/articles/InstallationGuide.html +++ b/articles/InstallationGuide.html @@ -6,19 +6,20 @@ Patient-Level Prediction Installation Guide • PatientLevelPrediction - + + - +
    - +
    @@ -152,7 +153,7 @@

    Jenna Reps,

    2024-09-09

    - Source: vignettes/InstallationGuide.Rmd + Source: vignettes/InstallationGuide.Rmd
    @@ -283,7 +284,8 @@

    AcknowledgmentsPatientLevelPrediction package.

     citation("PatientLevelPrediction")
    -
    ## To cite PatientLevelPrediction in publications use:
+
    ## 
+## To cite PatientLevelPrediction in publications use:
 ## 
 ##   Reps JM, Schuemie MJ, Suchard MA, Ryan PB, Rijnbeek P (2018). "Design
 ##   and implementation of a standardized framework to generate and
@@ -318,7 +320,9 @@ 
    Acknowledgments
 
-

    + +
    @@ -331,16 +335,16 @@

    Acknowledgments

    -

    Site built with pkgdown 2.1.0.

    +

    Site built with pkgdown 2.0.7.

    + - - + diff --git a/articles/Videos.html b/articles/Videos.html index e7c53b923..7277e6d0f 100644 --- a/articles/Videos.html +++ b/articles/Videos.html @@ -6,19 +6,20 @@ Demo Videos • PatientLevelPrediction - + + - +
    - +
    @@ -152,7 +153,7 @@

    Jenna Reps,

    2024-09-09

    - Source: vignettes/Videos.rmd + Source: vignettes/Videos.rmd
    @@ -283,7 +284,9 @@

    Validating existing models + +

    @@ -296,16 +299,16 @@

    Validating existing models

    -

    Site built with pkgdown 2.1.0.

    +

    Site built with pkgdown 2.0.7.

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    @@ -167,15 +167,15 @@

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    Site built with pkgdown 2.1.0.

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    - +
    -

    Authors and Citation

    +

    Authors

    - +
    • -

      Jenna Reps. Author, maintainer. +

      Jenna Reps. Author, maintainer.

    • -

      Martijn Schuemie. Author. +

      Martijn Schuemie. Author.

    • -

      Marc Suchard. Author. +

      Marc Suchard. Author.

    • -

      Patrick Ryan. Author. +

      Patrick Ryan. Author.

    • -

      Peter Rijnbeek. Author. +

      Peter Rijnbeek. Author.

    • -

      Egill Fridgeirsson. Author. +

      Egill Fridgeirsson. Author.

    Citation

    - Source: inst/CITATION + Source: inst/CITATION
    @@ -163,7 +163,7 @@

    Citation

    Reps JM, Schuemie MJ, Suchard MA, Ryan PB, Rijnbeek P (2018). “Design and implementation of a standardized framework to generate and evaluate patient-level prediction models using observational healthcare data.” Journal of the American Medical Informatics Association, 25(8), 969-975. -https://doi.org/10.1093/jamia/ocy032. +https://doi.org/10.1093/jamia/ocy032. 

    @Article{,
   author = {J. M. Reps and M. J. Schuemie and M. A. Suchard and P. B. Ryan and P. Rijnbeek},
@@ -187,15 +187,15 @@ 
    Citation
    -

    Site built with pkgdown 2.1.0.

    +

    Site built with pkgdown 2.0.7.

    + - - + diff --git a/dev/articles/AddingCustomFeatureEngineering.html b/dev/articles/AddingCustomFeatureEngineering.html index f8019eb31..304af680e 100644 --- a/dev/articles/AddingCustomFeatureEngineering.html +++ b/dev/articles/AddingCustomFeatureEngineering.html @@ -33,7 +33,7 @@ PatientLevelPrediction - 6.3.8.9999 + 6.3.9.9999 @@ -85,6 +85,9 @@
  • Building patient-level predictive models
    • +
  • + Clinical Models +
  • Constrained predictors
    • @@ -114,6 +117,9 @@
  • Best Practices
    • +
  • + Clinical Models +
  • Changelog
    • @@ -145,7 +151,7 @@

    Adding Custom Feature Engineering Functions

    Jenna Reps, Egill Fridgeirsson

    -

    2024-08-06

    +

    2024-10-02

    Source: vignettes/AddingCustomFeatureEngineering.Rmd @@ -372,7 +378,7 @@

    Acknowledgments

    -

    Site built with pkgdown 2.1.0.

    +

    Site built with pkgdown 2.1.1.

    diff --git a/dev/articles/AddingCustomModels.html b/dev/articles/AddingCustomModels.html index 2307f48f7..24fd908a3 100644 --- a/dev/articles/AddingCustomModels.html +++ b/dev/articles/AddingCustomModels.html @@ -33,7 +33,7 @@ PatientLevelPrediction - 6.3.8.9999 + 6.3.9.9999 @@ -85,6 +85,9 @@
  • Building patient-level predictive models
    • +
  • + Clinical Models +
  • Constrained predictors
    • @@ -114,6 +117,9 @@
  • Best Practices
    • +
  • + Clinical Models +
  • Changelog
    • @@ -146,7 +152,7 @@

    Adding Custom Patient-Level Prediction

    Jenna Reps, Martijn J. Schuemie, Patrick B. Ryan, Peter R. Rijnbeek

    -

    2024-08-06

    +

    2024-10-02

    Source: vignettes/AddingCustomModels.Rmd @@ -756,7 +762,7 @@

    Acknowledgments

    -

    Site built with pkgdown 2.1.0.

    +

    Site built with pkgdown 2.1.1.

    diff --git a/dev/articles/AddingCustomSamples.html b/dev/articles/AddingCustomSamples.html index e4bd04d2f..7a40bb075 100644 --- a/dev/articles/AddingCustomSamples.html +++ b/dev/articles/AddingCustomSamples.html @@ -33,7 +33,7 @@ PatientLevelPrediction - 6.3.8.9999 + 6.3.9.9999 @@ -85,6 +85,9 @@
  • Building patient-level predictive models
    • +
  • + Clinical Models +
  • Constrained predictors
    • @@ -114,6 +117,9 @@
  • Best Practices
    • +
  • + Clinical Models +
  • Changelog
    • @@ -144,7 +150,7 @@

    Adding Custom Sampling Functions

    Jenna Reps

    -

    2024-08-06

    +

    2024-10-02

    Source: vignettes/AddingCustomSamples.Rmd @@ -343,7 +349,7 @@

    Acknowledgments

    -

    Site built with pkgdown 2.1.0.

    +

    Site built with pkgdown 2.1.1.

    diff --git a/dev/articles/AddingCustomSplitting.html b/dev/articles/AddingCustomSplitting.html index ea4b71f87..745647f50 100644 --- a/dev/articles/AddingCustomSplitting.html +++ b/dev/articles/AddingCustomSplitting.html @@ -33,7 +33,7 @@ PatientLevelPrediction - 6.3.8.9999 + 6.3.9.9999 @@ -85,6 +85,9 @@
  • Building patient-level predictive models
    • +
  • + Clinical Models +
  • Constrained predictors
    • @@ -114,6 +117,9 @@
  • Best Practices
    • +
  • + Clinical Models +
  • Changelog
    • @@ -144,7 +150,7 @@

    Adding Custom Data Splitting Functions

    Jenna Reps

    -

    2024-08-06

    +

    2024-10-02

    Source: vignettes/AddingCustomSplitting.Rmd @@ -309,7 +315,7 @@

    Acknowledgments

    -

    Site built with pkgdown 2.1.0.

    +

    Site built with pkgdown 2.1.1.

    diff --git a/dev/articles/BenchmarkTasks.html b/dev/articles/BenchmarkTasks.html index 236ee7d1b..760c9d53c 100644 --- a/dev/articles/BenchmarkTasks.html +++ b/dev/articles/BenchmarkTasks.html @@ -33,7 +33,7 @@ PatientLevelPrediction - 6.3.8.9999 + 6.3.9.9999 @@ -85,6 +85,9 @@
  • Building patient-level predictive models
    • +
  • + Clinical Models +
  • Constrained predictors
    • @@ -114,6 +117,9 @@
  • Best Practices
    • +
  • + Clinical Models +
  • Changelog
    • @@ -145,7 +151,7 @@

    Benchmark Tasks

    Jenna Reps, Ross Williams, Peter R. Rijnbeek

    -

    2024-08-06

    +

    2024-10-02

    Source: vignettes/BenchmarkTasks.Rmd @@ -320,7 +326,7 @@

    Benchmark Tasks For

    -

    Site built with pkgdown 2.1.0.

    +

    Site built with pkgdown 2.1.1.

    diff --git a/dev/articles/BestPractices.html b/dev/articles/BestPractices.html index 48b4f6525..f03d8651c 100644 --- a/dev/articles/BestPractices.html +++ b/dev/articles/BestPractices.html @@ -33,7 +33,7 @@ PatientLevelPrediction - 6.3.8.9999 + 6.3.9.9999 @@ -85,6 +85,9 @@
  • Building patient-level predictive models
    • +
  • + Clinical Models +
  • Constrained predictors
    • @@ -114,6 +117,9 @@
  • Best Practices
    • +
  • + Clinical Models +
  • Changelog
    • @@ -145,7 +151,7 @@

    Best Practice Research

    Jenna Reps, Peter R. Rijnbeek

    -

    2024-08-06

    +

    2024-10-02

    Source: vignettes/BestPractices.rmd @@ -162,116 +168,255 @@

    2024-08-06

    Best practice publications using the OHDSI PatientLevelPrediction framework

    - ----- - - - - - - - - - - +
    TopicResearch SummaryLink
    Problem SpecificationWhen is prediction suitable in observational data?Guidelines needed
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - - - + + + + - - - - + + + + - - - - + + + + - - - - + + + + - - - - + + + + - - - - + + + + - - - - + + + + - - - - + + + + - - - - + + + + - - - - + + + + - - - - + + + + - - - - + + + + - - - - + + + + - - - - + + + + - - - - + + + + -
    +Topic + +Research Summary + +Link +
    +Problem Specification + +When is prediction suitable in observational data? + +Guidelines needed +
    +Data Creation + +Comparison of cohort vs case-control design + +Journal +of Big Data +
    +Data Creation + +Addressing loss to follow-up (right censoring) + +BMC +medical informatics and decision makingk +
    +Data Creation + +Investigating how to address left censoring in features construction + +BMC +Medical Research Methodology +
    +Data Creation + +Impact of over/under-sampling + + +Journal of big data +
    Data CreationComparison of cohort vs case-control designJournal -of Big Data
    +Data Creation + +Impact of phenotypes + +Study Done - Paper submitted +
    Data CreationAddressing loss to follow-up (right censoring)BMC -medical informatics and decision makingk
    +Model development + +How much data do we need for prediction - Learning curves at scale + +International +Journal of Medical Informatics +
    Data CreationInvestigating how to address left censoring in features -constructionBMC -Medical Research Methodology
    +Model development + +What impact does test/train/validation design have on model performance + +BMJ Open +
    Data CreationImpact of over/under-samplingPaper under review
    +Model development + +What is the impact of the classifier + +JAMIA +
    Data CreationImpact of phenotypesStudy Done - Paper submitted
    +Model development + +Can we find hyper-parameter combinations per classifier that +consistently lead to good performing models when using claims/EHR data? + +Study needs to be done +
    Model developmentHow much data do we need for prediction - Learning curves at -scaleInternational -Journal of Medical Informatics
    +Model development + +Can we use ensembles to combine different algorithm models within a +database to improve models transportability? + + Caring is +Sharing–Exploiting the Value in Data for Health and Innovation +
    Model developmentWhat impact does test/train/validation design have on model -performanceBMJ -Open
    +Model development + +Can we use ensembles to combine models developed using different +databases to improve models transportability? + + +BMC Medical Informatics and Decision Making +
    Model developmentWhat is the impact of the classifierJAMIA
    +Model development + +Impact of regularization method + + +JAMIA +
    Model developmentCan we find hyper-parameter combinations per classifier that -consistently lead to good performing models when using claims/EHR -data?Study needs to be done
    +Evaluation + +Why prediction is not suitable for risk factor identification + + Machine +Learning for Healthcare Conference +
    Model developmentCan we use ensembles to combine different algorithm models within a -database to improve models transportability?Study Complete
    +Evaluation + +Iterative pairwise external validation to put validation into context + + +Drug Safety +
    Model developmentCan we use ensembles to combine models developed using different -databases to improve models transportability?BMC -Medical Informatics and Decision Making
    +Evaluation + +A novel method to estimate external validation using aggregate +statistics + + Study under review +
    EvaluationHow should we present model performance? (e.g., new -visualizations)JAMIA -Open
    +Evaluation + +How should we present model performance? (e.g., new visualizations) + +JAMIA +Open +
    EvaluationHow to interpret external validation performance (can we figure out -why the performance drops or stays consistent)?Study needs to be done
    +Evaluation + +How to interpret external validation performance (can we figure out why +the performance drops or stays consistent)? + +Study needs to be done +
    EvaluationRecalibration methodsStudy needs to be done
    +Evaluation + +Recalibration methods + +Study needs to be done +
    EvaluationIs there a way to automatically simplify models?Study -protocol under development
    +Evaluation + +Is there a way to automatically simplify models? + +Study +protocol under development +
    @@ -291,7 +436,7 @@

    -

    Site built with pkgdown 2.1.0.

    +

    Site built with pkgdown 2.1.1.

    diff --git a/dev/articles/BuildingMultiplePredictiveModels.html b/dev/articles/BuildingMultiplePredictiveModels.html index d46f196c7..b731a85d8 100644 --- a/dev/articles/BuildingMultiplePredictiveModels.html +++ b/dev/articles/BuildingMultiplePredictiveModels.html @@ -33,7 +33,7 @@ PatientLevelPrediction - 6.3.8.9999 + 6.3.9.9999 @@ -85,6 +85,9 @@
  • Building patient-level predictive models
    • +
  • + Clinical Models +
  • Constrained predictors
    • @@ -114,6 +117,9 @@
  • Best Practices
    • +
  • + Clinical Models +
  • Changelog
    • @@ -146,7 +152,7 @@

    Automatically Build Multiple Patient-Level

    Jenna Reps, Martijn J. Schuemie, Patrick B. Ryan, Peter R. Rijnbeek

    -

    2024-08-06

    +

    2024-10-02

    Source: vignettes/BuildingMultiplePredictiveModels.Rmd @@ -543,7 +549,7 @@

    Acknowledgments

    -

    Site built with pkgdown 2.1.0.

    +

    Site built with pkgdown 2.1.1.

    diff --git a/dev/articles/BuildingPredictiveModels.html b/dev/articles/BuildingPredictiveModels.html index 4bd14554f..20f21997e 100644 --- a/dev/articles/BuildingPredictiveModels.html +++ b/dev/articles/BuildingPredictiveModels.html @@ -33,7 +33,7 @@ PatientLevelPrediction - 6.3.8.9999 + 6.3.9.9999 @@ -85,6 +85,9 @@
  • Building patient-level predictive models
    • +
  • + Clinical Models +
  • Constrained predictors
    • @@ -114,6 +117,9 @@
  • Best Practices
    • +
  • + Clinical Models +
  • Changelog
    • @@ -145,7 +151,7 @@

    Building patient-level predictive models

    Jenna Reps, Martijn J. Schuemie, Patrick B. Ryan, Peter R. Rijnbeek

    -

    2024-08-06

    +

    2024-10-02

    Source: vignettes/BuildingPredictiveModels.Rmd @@ -690,7 +696,7 @@

    Cohort instantiationATLAS cohort builder

    -Target Cohort Atrial Fibrillation
    Target Cohort Atrial Fibrillation
    +Target Cohort Atrial Fibrillation
    Target Cohort Atrial Fibrillation

    ATLAS allows you to define cohorts interactively by specifying cohort entry and cohort exit criteria. Cohort entry criteria involve selecting @@ -703,7 +709,7 @@

    ATLAS cohort builder -Outcome Cohort Stroke
    Outcome Cohort Stroke
    +Outcome Cohort Stroke
    Outcome Cohort Stroke

    The T and O cohorts can be found here:

      @@ -1303,7 +1309,7 @@

      Cohort instantiationATLAS cohort builder

      -Target Cohort ACE inhibitors
      Target Cohort ACE inhibitors
      +Target Cohort ACE inhibitors
      Target Cohort ACE inhibitors

      ATLAS allows you to define cohorts interactively by specifying cohort entry and cohort exit criteria. Cohort entry criteria involve selecting @@ -1316,7 +1322,7 @@

      ATLAS cohort builder
      -Outcome Cohort Angioedema
      Outcome Cohort Angioedema
      +Outcome Cohort Angioedema
      Outcome Cohort Angioedema

      The T and O cohorts can be found here:

       -
      sampleSettings
      +
      sampleSettings

      An object of type sampleSettings that specifies any under/over sampling to be done. The default is none.

      -
      featureEngineeringSettings
      +
      featureEngineeringSettings

      An object of featureEngineeringSettings specifying any feature engineering to be learned (using the train data)

      -
      preprocessSettings
      -

      An object of preprocessSettings. This setting specifies the minimum fraction of -target population who must have a covariate for it to be included in the model training +

      preprocessSettings
      +

      An object of preprocessSettings. This setting specifies the minimum fraction of +target population who must have a covariate for it to be included in the model training and whether to normalise the covariates before training

      -
      logSettings
      -

      An object of logSettings created using createLogSettings +

      logSettings
      +

      An object of logSettings created using createLogSettings specifying how the logging is done

      -
      executeSettings
      +
      executeSettings

      An object of executeSettings specifying which parts of the analysis to run

      Value

      -

      A learning curve object containing the various performance measures + + +

      A learning curve object containing the various performance measures obtained by the model for each training set fraction. It can be plotted using plotLearningCurve.

      Examples

      - 
      if (FALSE) { # \dontrun{
+    
      if (FALSE) {
 # define model
 modelSettings = PatientLevelPrediction::setLassoLogisticRegression()
 
@@ -263,7 +263,7 @@ 
      Examples
      
                                                              modelSettings)
 # plot learning curve
 PatientLevelPrediction::plotLearningCurve(learningCurve)
-} # }
+}
      @@ -279,15 +279,15 @@

      Examples

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/createLogSettings.html b/reference/createLogSettings.html index be996eb45..05c7e8c73 100644 --- a/reference/createLogSettings.html +++ b/reference/createLogSettings.html @@ -1,9 +1,9 @@ -Create the settings for logging the progression of the analysis — createLogSettings • PatientLevelPredictionCreate the settings for logging the progression of the analysis — createLogSettings • PatientLevelPrediction - +
      - +

      Create the settings for logging the progression of the analysis

      - Source: R/Logging.R + Source: R/Logging.R
      @@ -137,9 +137,7 @@

      Create the settings for logging the progression of the analysis

      Arguments

      - - -
      verbosity
      +
      verbosity

      Sets the level of the verbosity. If the log level is at or higher in priority than the logger threshold, a message will print. The levels are:

      • DEBUG Highest verbosity showing all debug statements

      • TRACE Showing information about start and end of steps

      • INFO Show informative information (Default)

        • @@ -149,17 +147,21 @@

        Arguments

      -
      timeStamp
      +
      timeStamp

      If TRUE a timestamp will be added to each logging statement. Automatically switched on for TRACE level.

      -
      logName
      +
      logName

      A string reference for the logger

      Value

      -

      An object of class logSettings

      + + +

      An object of class logSettings

      + +

      Details

      @@ -178,15 +180,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/createModelDesign.html b/reference/createModelDesign.html index ff58ae1d1..f1fdfafec 100644 --- a/reference/createModelDesign.html +++ b/reference/createModelDesign.html @@ -1,9 +1,9 @@ -Specify settings for deceloping a single model — createModelDesign • PatientLevelPredictionSpecify settings for deceloping a single model — createModelDesign • PatientLevelPrediction - +
      - +

      Specify settings for deceloping a single model

      - Source: R/RunMultiplePlp.R + Source: R/RunMultiplePlp.R
      @@ -150,55 +150,55 @@

      Specify settings for deceloping a single model

      Arguments

      - - -
      targetId
      +
      targetId

      The id of the target cohort that will be used for data extraction (e.g., the ATLAS id)

      -
      outcomeId
      +
      outcomeId

      The id of the outcome that will be used for data extraction (e.g., the ATLAS id)

      -
      restrictPlpDataSettings
      +
      restrictPlpDataSettings

      The settings specifying the extra restriction settings when extracting the data created using createRestrictPlpDataSettings().

      -
      populationSettings
      +
      populationSettings

      The population settings specified by createStudyPopulationSettings()

      -
      covariateSettings
      +
      covariateSettings

      The covariate settings, this can be a list or a single 'covariateSetting' object.

      -
      featureEngineeringSettings
      +
      featureEngineeringSettings

      Either NULL or an object of class featureEngineeringSettings specifying any feature engineering used during model development

      -
      sampleSettings
      +
      sampleSettings

      Either NULL or an object of class sampleSettings with the over/under sampling settings used for model development

      -
      preprocessSettings
      +
      preprocessSettings

      Either NULL or an object of class preprocessSettings created using createPreprocessingSettings()

      -
      modelSettings
      +
      modelSettings

      The model settings such as setLassoLogisticRegression()

      -
      splitSettings
      +
      splitSettings

      The train/validation/test splitting used by all analyses created using createDefaultSplitSetting()

      -
      runCovariateSummary
      +
      runCovariateSummary

      Whether to run the covariateSummary

      Value

      -

      A list with analysis settings used to develop a single prediction model

      + + +

      A list with analysis settings used to develop a single prediction model

      Details

      @@ -217,15 +217,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/createPlpResultTables.html b/reference/createPlpResultTables.html index e337922e1..41c22b897 100644 --- a/reference/createPlpResultTables.html +++ b/reference/createPlpResultTables.html @@ -1,9 +1,9 @@ -Create the results tables to store PatientLevelPrediction models and results into a database — createPlpResultTables • PatientLevelPredictionCreate the results tables to store PatientLevelPrediction models and results into a database — createPlpResultTables • PatientLevelPrediction - +
      - +

      Create the results tables to store PatientLevelPrediction models and results into a database

      - Source: R/uploadToDatabase.R + Source: R/uploadToDatabase.R
      @@ -146,43 +146,43 @@

      Create the results tables to store PatientLevelPrediction models and results

      Arguments

      - - -
      connectionDetails
      +
      connectionDetails

      The database connection details

      -
      targetDialect
      +
      targetDialect

      The database management system being used

      -
      resultSchema
      +
      resultSchema

      The name of the database schema that the result tables will be created.

      -
      deleteTables
      +
      deleteTables

      If true any existing tables matching the PatientLevelPrediction result tables names will be deleted

      -
      createTables
      +
      createTables

      If true the PatientLevelPrediction result tables will be created

      -
      tablePrefix
      +
      tablePrefix

      A string that appends to the PatientLevelPrediction result tables

      -
      tempEmulationSchema
      +
      tempEmulationSchema

      The temp schema used when the database management system is oracle

      -
      testFile
      +
      testFile

      (used for testing) The location of an sql file with the table creation code

      Value

      -

      Returns NULL but creates the required tables into the specified database schema(s).

      + + +

      Returns NULL but creates the required tables into the specified database schema(s).

      Details

      @@ -201,15 +201,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/createPreprocessSettings.html b/reference/createPreprocessSettings.html index c36725cca..74d078c2b 100644 --- a/reference/createPreprocessSettings.html +++ b/reference/createPreprocessSettings.html @@ -1,9 +1,9 @@ -Create the settings for preprocessing the trainData. — createPreprocessSettings • PatientLevelPredictionCreate the settings for preprocessing the trainData. — createPreprocessSettings • PatientLevelPrediction - +
      - +

      Create the settings for preprocessing the trainData.

      - Source: R/PreprocessingData.R + Source: R/PreprocessingData.R
      @@ -141,23 +141,25 @@

      Create the settings for preprocessing the trainData.

      Arguments

      - - -
      minFraction
      +
      minFraction

      The minimum fraction of target population who must have a covariate for it to be included in the model training

      -
      normalize
      +
      normalize

      Whether to normalise the covariates before training (Default: TRUE)

      -
      removeRedundancy
      +
      removeRedundancy

      Whether to remove redundant features (Default: TRUE)

      Value

      -

      An object of class preprocessingSettings

      + + +

      An object of class preprocessingSettings

      + +

      Details

      @@ -176,15 +178,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/createRandomForestFeatureSelection.html b/reference/createRandomForestFeatureSelection.html index 490ffcc53..3b2803c55 100644 --- a/reference/createRandomForestFeatureSelection.html +++ b/reference/createRandomForestFeatureSelection.html @@ -1,9 +1,9 @@ -Create the settings for random foreat based feature selection — createRandomForestFeatureSelection • PatientLevelPredictionCreate the settings for random foreat based feature selection — createRandomForestFeatureSelection • PatientLevelPrediction - +
      - +

      Create the settings for random foreat based feature selection

      - Source: R/FeatureEngineering.R + Source: R/FeatureEngineering.R
      @@ -137,19 +137,21 @@

      Create the settings for random foreat based feature selection

      Arguments

      - - -
      ntrees
      +
      ntrees

      number of tree in forest

      -
      maxDepth
      +
      maxDepth

      MAx depth of each tree

      Value

      -

      An object of class featureEngineeringSettings

      + + +

      An object of class featureEngineeringSettings

      + +

      Details

      @@ -168,15 +170,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/createRestrictPlpDataSettings.html b/reference/createRestrictPlpDataSettings.html index 141206f42..923489204 100644 --- a/reference/createRestrictPlpDataSettings.html +++ b/reference/createRestrictPlpDataSettings.html @@ -1,9 +1,9 @@ -createRestrictPlpDataSettings define extra restriction settings when calling getPlpData — createRestrictPlpDataSettings • PatientLevelPredictioncreateRestrictPlpDataSettings define extra restriction settings when calling getPlpData — createRestrictPlpDataSettings • PatientLevelPrediction - +
      - +

      createRestrictPlpDataSettings define extra restriction settings when calling getPlpData

      - Source: R/ExtractData.R + Source: R/ExtractData.R
      @@ -143,40 +143,40 @@

      createRestrictPlpDataSettings define extra restriction settings when calling

      Arguments

      - - -
      studyStartDate
      +
      studyStartDate

      A calendar date specifying the minimum date that a cohort index date can appear. Date format is 'yyyymmdd'.

      -
      studyEndDate
      +
      studyEndDate

      A calendar date specifying the maximum date that a cohort index date can appear. Date format is 'yyyymmdd'. Important: the study end data is also used to truncate risk windows, meaning no outcomes beyond the study end date will be considered.

      -
      firstExposureOnly
      +
      firstExposureOnly

      Should only the first exposure per subject be included? Note that this is typically done in the createStudyPopulation function, but can already be done here for efficiency reasons.

      -
      washoutPeriod
      +
      washoutPeriod

      The mininum required continuous observation time prior to index date for a person to be included in the at risk cohort. Note that this is typically done in the createStudyPopulation function, but can already be done here for efficiency reasons.

      -
      sampleSize
      +
      sampleSize

      If not NULL, the number of people to sample from the target cohort

      Value

      -

      A setting object of class restrictPlpDataSettings containing a list getPlpData extra settings

      + + +

      A setting object of class restrictPlpDataSettings containing a list getPlpData extra settings

      Details

      @@ -195,15 +195,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/createSampleSettings.html b/reference/createSampleSettings.html index c209415ee..f2723ab9e 100644 --- a/reference/createSampleSettings.html +++ b/reference/createSampleSettings.html @@ -1,10 +1,12 @@ -Create the settings for defining how the trainData from splitData are sampled using default sample functions. — createSampleSettings • PatientLevelPredictionCreate the settings for defining how the trainData from splitData are sampled using +default sample functions. — createSampleSettings • PatientLevelPrediction - +
      - +
      -

      Create the settings for defining how the trainData from splitData are sampled using default sample functions.

      - Source: R/Sampling.R +

      Create the settings for defining how the trainData from splitData are sampled using +default sample functions.

      + Source: R/Sampling.R
      -

      Create the settings for defining how the trainData from splitData are sampled using +

      Create the settings for defining how the trainData from splitData are sampled using default sample functions.

      @@ -143,26 +146,28 @@

      Create the settings for defining how the trainData from splitData

      Arguments

      - - -
      type
      +
      type

      (character) Choice of:

      • 'none' No sampling is applied - this is the default

      • 'underSample' Undersample the non-outcome class to make the data more ballanced

      • 'overSample' Oversample the outcome class by adding in each outcome multiple times

      -
      numberOutcomestoNonOutcomes
      +
      numberOutcomestoNonOutcomes

      (numeric) An numeric specifying the require number of non-outcomes per outcome

      -
      sampleSeed
      +
      sampleSeed

      (numeric) A seed to use when splitting the data for reproducibility (if not set a random number will be generated)

      Value

      -

      An object of class sampleSettings

      + + +

      An object of class sampleSettings

      + +

      Details

      @@ -181,15 +186,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/createSplineSettings.html b/reference/createSplineSettings.html index 65b951d70..9b04d1a1b 100644 --- a/reference/createSplineSettings.html +++ b/reference/createSplineSettings.html @@ -1,9 +1,9 @@ -Create the settings for adding a spline for continuous variables — createSplineSettings • PatientLevelPredictionCreate the settings for adding a spline for continuous variables — createSplineSettings • PatientLevelPrediction - +
      - +

      Create the settings for adding a spline for continuous variables

      - Source: R/FeatureEngineering.R + Source: R/FeatureEngineering.R
      @@ -137,23 +137,25 @@

      Create the settings for adding a spline for continuous variables

      Arguments

      - - -
      continousCovariateId
      +
      continousCovariateId

      The covariateId to apply splines to

      -
      knots
      +
      knots

      Either number of knots of vector of split values

      -
      analysisId
      +
      analysisId

      The analysisId to use for the spline covariates

      Value

      -

      An object of class featureEngineeringSettings

      + + +

      An object of class featureEngineeringSettings

      + +

      Details

      @@ -172,15 +174,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/createStratifiedImputationSettings.html b/reference/createStratifiedImputationSettings.html index b1506e51d..753176d9b 100644 --- a/reference/createStratifiedImputationSettings.html +++ b/reference/createStratifiedImputationSettings.html @@ -1,9 +1,9 @@ -Create the settings for adding a spline for continuous variables — createStratifiedImputationSettings • PatientLevelPredictionCreate the settings for adding a spline for continuous variables — createStratifiedImputationSettings • PatientLevelPrediction - +
      - +

      Create the settings for adding a spline for continuous variables

      - Source: R/FeatureEngineering.R + Source: R/FeatureEngineering.R
      @@ -137,19 +137,21 @@

      Create the settings for adding a spline for continuous variables

      Arguments

      - - -
      covariateId
      +
      covariateId

      The covariateId that needs imputed values

      -
      ageSplits
      +
      ageSplits

      A vector of age splits in years to create age groups

      Value

      -

      An object of class featureEngineeringSettings

      + + +

      An object of class featureEngineeringSettings

      + +

      Details

      @@ -168,15 +170,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/createStudyPopulation.html b/reference/createStudyPopulation.html index db8b2b0d7..70fb48349 100644 --- a/reference/createStudyPopulation.html +++ b/reference/createStudyPopulation.html @@ -1,9 +1,9 @@ -Create a study population — createStudyPopulation • PatientLevelPredictionCreate a study population — createStudyPopulation • PatientLevelPrediction - +
      - +

      Create a study population

      - Source: R/PopulationSettings.R + Source: R/PopulationSettings.R
      @@ -142,29 +142,29 @@

      Create a study population

      Arguments

      - - -
      plpData
      +
      plpData

      An object of type plpData as generated using getplpData.

      -
      outcomeId
      +
      outcomeId

      The ID of the outcome.

      -
      populationSettings
      +
      populationSettings

      An object of class populationSettings created using createPopulationSettings

      -
      population
      +
      population

      If specified, this population will be used as the starting point instead of the cohorts in the plpData object.

      Value

      -

      A data frame specifying the study population. This data frame will have the following columns:

      rowId
      + + +

      A data frame specifying the study population. This data frame will have the following columns:

      rowId

      A unique identifier for an exposure

      subjectId
      @@ -202,15 +202,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/createStudyPopulationSettings.html b/reference/createStudyPopulationSettings.html index ee8949fa3..e40884cc2 100644 --- a/reference/createStudyPopulationSettings.html +++ b/reference/createStudyPopulationSettings.html @@ -1,9 +1,9 @@ -create the study population settings — createStudyPopulationSettings • PatientLevelPredictioncreate the study population settings — createStudyPopulationSettings • PatientLevelPrediction - +
      - +

      create the study population settings

      - Source: R/PopulationSettings.R + Source: R/PopulationSettings.R
      @@ -151,69 +151,69 @@

      create the study population settings

      Arguments

      - - -
      binary
      +
      binary

      Forces the outcomeCount to be 0 or 1 (use for binary prediction problems)

      -
      includeAllOutcomes
      +
      includeAllOutcomes

      (binary) indicating whether to include people with outcomes who are not observed for the whole at risk period

      -
      firstExposureOnly
      +
      firstExposureOnly

      Should only the first exposure per subject be included? Note that this is typically done in the createStudyPopulation function,

      -
      washoutPeriod
      +
      washoutPeriod

      The mininum required continuous observation time prior to index date for a person to be included in the cohort.

      -
      removeSubjectsWithPriorOutcome
      +
      removeSubjectsWithPriorOutcome

      Remove subjects that have the outcome prior to the risk window start?

      -
      priorOutcomeLookback
      +
      priorOutcomeLookback

      How many days should we look back when identifying prior outcomes?

      -
      requireTimeAtRisk
      +
      requireTimeAtRisk

      Should subject without time at risk be removed?

      -
      minTimeAtRisk
      +
      minTimeAtRisk

      The minimum number of days at risk required to be included

      -
      riskWindowStart
      +
      riskWindowStart

      The start of the risk window (in days) relative to the index date (+ days of exposure if the addExposureDaysToStart parameter is specified).

      -
      startAnchor
      +
      startAnchor

      The anchor point for the start of the risk window. Can be "cohort start" or "cohort end".

      -
      riskWindowEnd
      +
      riskWindowEnd

      The end of the risk window (in days) relative to the index data (+ days of exposure if the addExposureDaysToEnd parameter is specified).

      -
      endAnchor
      +
      endAnchor

      The anchor point for the end of the risk window. Can be "cohort start" or "cohort end".

      -
      restrictTarToCohortEnd
      +
      restrictTarToCohortEnd

      If using a survival model and you want the time-at-risk to end at the cohort end date set this to T

      Value

      -

      A list containing all the settings required for creating the study population

      + + +

      A list containing all the settings required for creating the study population

      Details

      @@ -232,15 +232,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/createTempModelLoc.html b/reference/createTempModelLoc.html index a14dd0a74..e91a74cf3 100644 --- a/reference/createTempModelLoc.html +++ b/reference/createTempModelLoc.html @@ -1,9 +1,9 @@ -Create a temporary model location — createTempModelLoc • PatientLevelPredictionCreate a temporary model location — createTempModelLoc • PatientLevelPrediction - +
      - +

      Create a temporary model location

      - Source: R/HelperFunctions.R + Source: R/HelperFunctions.R
      @@ -148,15 +148,15 @@

      Create a temporary model location

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/createUnivariateFeatureSelection.html b/reference/createUnivariateFeatureSelection.html index af939a07b..4f4c8fb19 100644 --- a/reference/createUnivariateFeatureSelection.html +++ b/reference/createUnivariateFeatureSelection.html @@ -1,9 +1,9 @@ -Create the settings for defining any feature selection that will be done — createUnivariateFeatureSelection • PatientLevelPredictionCreate the settings for defining any feature selection that will be done — createUnivariateFeatureSelection • PatientLevelPrediction - +
      - +

      Create the settings for defining any feature selection that will be done

      - Source: R/FeatureEngineering.R + Source: R/FeatureEngineering.R
      @@ -137,15 +137,17 @@

      Create the settings for defining any feature selection that will be done

      Arguments

      - - -
      k
      +
      k

      This function returns the K features most associated (univariately) to the outcome

      Value

      -

      An object of class featureEngineeringSettings

      + + +

      An object of class featureEngineeringSettings

      + +

      Details

      @@ -164,15 +166,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/createValidationDesign.html b/reference/createValidationDesign.html index f3101e2ba..f3601d2c9 100644 --- a/reference/createValidationDesign.html +++ b/reference/createValidationDesign.html @@ -1,9 +1,9 @@ -createValidationDesign - Define the validation design for external validation — createValidationDesign • PatientLevelPredictioncreateValidationDesign - Define the validation design for external validation — createValidationDesign • PatientLevelPrediction - +
      - +

      createValidationDesign - Define the validation design for external validation

      - Source: R/ExternalValidatePlp.R + Source: R/ExternalValidatePlp.R
      @@ -145,33 +145,31 @@

      createValidationDesign - Define the validation design for external validatio

      Arguments

      - - -
      targetId
      +
      targetId

      The targetId of the target cohort to validate on

      -
      outcomeId
      +
      outcomeId

      The outcomeId of the outcome cohort to validate on

      -
      populationSettings
      +
      populationSettings

      A list of population restriction settings created by createPopulationSettings

      -
      restrictPlpDataSettings
      +
      restrictPlpDataSettings

      A list of plpData restriction settings created by createRestrictPlpDataSettings

      -
      plpModelList
      +
      plpModelList

      A list of plpModels objects created by runPlp or a path to such objects

      -
      recalibrate
      +
      recalibrate

      A vector of characters specifying the recalibration method to apply,

      -
      runCovariateSummary
      +
      runCovariateSummary

      whether to run the covariate summary for the validation data

      @@ -188,15 +186,15 @@

      Arguments

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/createValidationSettings.html b/reference/createValidationSettings.html index 89dd6d47c..c21539127 100644 --- a/reference/createValidationSettings.html +++ b/reference/createValidationSettings.html @@ -1,9 +1,9 @@ -createValidationSettings define optional settings for performing external validation — createValidationSettings • PatientLevelPredictioncreateValidationSettings define optional settings for performing external validation — createValidationSettings • PatientLevelPrediction - +
      - +

      createValidationSettings define optional settings for performing external validation

      - Source: R/ExternalValidatePlp.R + Source: R/ExternalValidatePlp.R
      @@ -137,19 +137,19 @@

      createValidationSettings define optional settings for performing external va

      Arguments

      - - -
      recalibrate
      +
      recalibrate

      A vector of characters specifying the recalibration method to apply

      -
      runCovariateSummary
      +
      runCovariateSummary

      Whether to run the covariate summary for the validation data

      Value

      -

      A setting object of class validationSettings containing a list of settings for externalValidatePlp

      + + +

      A setting object of class validationSettings containing a list of settings for externalValidatePlp

      Details

      @@ -168,15 +168,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/diagnoseMultiplePlp.html b/reference/diagnoseMultiplePlp.html index af5515a59..5c24f5c05 100644 --- a/reference/diagnoseMultiplePlp.html +++ b/reference/diagnoseMultiplePlp.html @@ -1,9 +1,9 @@ -Run a list of predictions diagnoses — diagnoseMultiplePlp • PatientLevelPredictionRun a list of predictions diagnoses — diagnoseMultiplePlp • PatientLevelPrediction - +
      - +

      Run a list of predictions diagnoses

      - Source: R/DiagnosePlp.R + Source: R/DiagnosePlp.R
      @@ -146,31 +146,31 @@

      Run a list of predictions diagnoses

      Arguments

      - - -
      databaseDetails
      +
      databaseDetails

      The database settings created using createDatabaseDetails()

      -
      modelDesignList
      +
      modelDesignList

      A list of model designs created using createModelDesign()

      -
      cohortDefinitions
      +
      cohortDefinitions

      A list of cohort definitions for the target and outcome cohorts

      -
      logSettings
      +
      logSettings

      The setting spexcifying the logging for the analyses created using createLogSettings()

      -
      saveDirectory
      +
      saveDirectory

      Name of the folder where all the outputs will written to.

      Value

      -

      A data frame with the following columns:

      - + - + @@ -372,7 +374,8 @@

      Individual pipeline functions preprocessData()

      -

      + @@ -472,7 +475,8 @@

      Plotting

      - + - + @@ -746,15 +753,15 @@

      Other functions -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/insertCsvToDatabase.html b/reference/insertCsvToDatabase.html index 160b96d18..80fd8e6cf 100644 --- a/reference/insertCsvToDatabase.html +++ b/reference/insertCsvToDatabase.html @@ -1,9 +1,9 @@ -Function to insert results into a database from csvs — insertCsvToDatabase • PatientLevelPredictionFunction to insert results into a database from csvs — insertCsvToDatabase • PatientLevelPrediction - +
      - +

      Function to insert results into a database from csvs

      - Source: R/ImportFromCsv.R + Source: R/ImportFromCsv.R
      @@ -143,31 +143,31 @@

      Function to insert results into a database from csvs

      Arguments

      - - -
      csvFolder
      +
      csvFolder

      The location to the csv folder with the plp results

      -
      connectionDetails
      +
      connectionDetails

      A connection details for the plp results database that the csv results will be inserted into

      -
      databaseSchemaSettings
      +
      databaseSchemaSettings

      A object created by createDatabaseSchemaSettings with all the settings specifying the result tables to insert the csv results into

      -
      modelSaveLocation
      +
      modelSaveLocation

      The location to save any models from the csv folder - this should be the same location you picked when inserting other models into the database

      -
      csvTableAppend
      +
      csvTableAppend

      A string that appends the csv file names

      Value

      -

      Returns a data.frame indicating whether the results were inported into the database

      + + +

      Returns a data.frame indicating whether the results were inported into the database

      Details

      @@ -186,15 +186,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/insertModelDesignInDatabase.html b/reference/insertModelDesignInDatabase.html index d0557cd14..a69c6aa60 100644 --- a/reference/insertModelDesignInDatabase.html +++ b/reference/insertModelDesignInDatabase.html @@ -1,9 +1,9 @@ -Insert a model design into a PLP result schema database — insertModelDesignInDatabase • PatientLevelPredictionInsert a model design into a PLP result schema database — insertModelDesignInDatabase • PatientLevelPrediction - +
      - +

      Insert a model design into a PLP result schema database

      - Source: R/uploadToDatabaseModelDesign.R + Source: R/uploadToDatabaseModelDesign.R
      @@ -142,29 +142,29 @@

      Insert a model design into a PLP result schema database

      Arguments

      - - -
      object
      +
      object

      An object of class modelDesign, runPlp or externalValidatePlp

      -
      conn
      +
      conn

      A connection to a database created by using the function connect in the DatabaseConnector package.

      -
      databaseSchemaSettings
      +
      databaseSchemaSettings

      A object created by createDatabaseSchemaSettings with all the settings specifying the result tables

      -
      cohortDefinitions
      +
      cohortDefinitions

      A set of one or more cohorts extracted using ROhdsiWebApi::exportCohortDefinitionSet()

      Value

      -

      Returns NULL but uploads the model design into the database schema specified in databaseSchemaSettings

      + + +

      Returns NULL but uploads the model design into the database schema specified in databaseSchemaSettings

      Details

      @@ -183,15 +183,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/insertResultsToSqlite.html b/reference/insertResultsToSqlite.html index 7a8a990ca..1fe355a82 100644 --- a/reference/insertResultsToSqlite.html +++ b/reference/insertResultsToSqlite.html @@ -1,9 +1,9 @@ -Create sqlite database with the results — insertResultsToSqlite • PatientLevelPredictionCreate sqlite database with the results — insertResultsToSqlite • PatientLevelPrediction - +
      - +

      Create sqlite database with the results

      - Source: R/uploadToDatabase.R + Source: R/uploadToDatabase.R
      @@ -142,27 +142,27 @@

      Create sqlite database with the results

      Arguments

      - - -
      resultLocation
      +
      resultLocation

      (string) location of directory where the main package results were saved

      -
      cohortDefinitions
      +
      cohortDefinitions

      A set of one or more cohorts extracted using ROhdsiWebApi::exportCohortDefinitionSet()

      -
      databaseList
      +
      databaseList

      A list created by createDatabaseList to specify the databases

      -
      sqliteLocation
      +
      sqliteLocation

      (string) location of directory where the sqlite database will be saved

      Value

      -

      Returns the location of the sqlite database file

      + + +

      Returns the location of the sqlite database file

      Details

      @@ -181,15 +181,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/listAppend.html b/reference/listAppend.html index 5d1ede4dd..2f370ad63 100644 --- a/reference/listAppend.html +++ b/reference/listAppend.html @@ -1,9 +1,9 @@ -join two lists — listAppend • PatientLevelPredictionjoin two lists — listAppend • PatientLevelPrediction - +
      - +

      join two lists

      - Source: R/HelperFunctions.R + Source: R/HelperFunctions.R
      @@ -137,13 +137,11 @@

      join two lists

      Arguments

      - - -
      a
      +
      a

      A list

      -
      b
      +
      b

      Another list

      @@ -164,15 +162,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/listCartesian.html b/reference/listCartesian.html index 82522b7d6..5c5c573b7 100644 --- a/reference/listCartesian.html +++ b/reference/listCartesian.html @@ -1,9 +1,9 @@ -Cartesian product — listCartesian • PatientLevelPredictionCartesian product — listCartesian • PatientLevelPrediction - +
      - +

      Cartesian product

      - Source: R/SklearnClassifierHelpers.R + Source: R/SklearnClassifierHelpers.R
      @@ -137,15 +137,15 @@

      Cartesian product

      Arguments

      - - -
      allList
      +
      allList

      a list of lists

      Value

      -

      A list with all possible combinations from the input list of lists

      + + +

      A list with all possible combinations from the input list of lists

      @@ -160,15 +160,15 @@

      Value

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/loadPlpAnalysesJson.html b/reference/loadPlpAnalysesJson.html index acbb8321f..4d1a0a0cc 100644 --- a/reference/loadPlpAnalysesJson.html +++ b/reference/loadPlpAnalysesJson.html @@ -1,9 +1,9 @@ -Load the multiple prediction json settings from a file — loadPlpAnalysesJson • PatientLevelPredictionLoad the multiple prediction json settings from a file — loadPlpAnalysesJson • PatientLevelPrediction - +
      - +

      Load the multiple prediction json settings from a file

      - Source: R/RunMultiplePlp.R + Source: R/RunMultiplePlp.R
      @@ -137,23 +137,21 @@

      Load the multiple prediction json settings from a file

      Arguments

      - - -
      jsonFileLocation
      +
      jsonFileLocation

      The location of the file 'predictionAnalysisList.json' with the modelDesignList

      Details

      -

      This function interprets a json with the multiple prediction settings and creates a list +

      This function interprets a json with the multiple prediction settings and creates a list that can be combined with connection settings to run a multiple prediction study

      Examples

      - 
      if (FALSE) { # \dontrun{
+    
      if (FALSE) {
 modelDesignList <- loadPlpAnalysesJson('location of json settings')$analysis
-} # }
+}
      @@ -169,15 +167,15 @@

      Examples

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/loadPlpData.html b/reference/loadPlpData.html index cf1e148bf..ab34cac18 100644 --- a/reference/loadPlpData.html +++ b/reference/loadPlpData.html @@ -1,10 +1,10 @@ -Load the cohort data from a folder — loadPlpData • PatientLevelPredictionLoad the cohort data from a folder — loadPlpData • PatientLevelPrediction - +
      - +

      Load the cohort data from a folder

      - Source: R/SaveLoadPlp.R + Source: R/SaveLoadPlp.R
      @@ -139,19 +139,19 @@

      Load the cohort data from a folder

      Arguments

      - - -
      file
      +
      file

      The name of the folder containing the data.

      -
      readOnly
      +
      readOnly

      If true, the data is opened read only.

      Value

      -

      An object of class plpData.

      + + +

      An object of class plpData.

      Details

      @@ -177,15 +177,15 @@

      Examples

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/loadPlpModel.html b/reference/loadPlpModel.html index 61a91d438..3248a457e 100644 --- a/reference/loadPlpModel.html +++ b/reference/loadPlpModel.html @@ -1,9 +1,9 @@ -loads the plp model — loadPlpModel • PatientLevelPredictionloads the plp model — loadPlpModel • PatientLevelPrediction - +
      - +

      loads the plp model

      - Source: R/SaveLoadPlp.R + Source: R/SaveLoadPlp.R
      @@ -137,9 +137,7 @@

      loads the plp model

      Arguments

      - - -
      dirPath
      +
      dirPath

      The location of the model

      @@ -160,15 +158,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/loadPlpResult.html b/reference/loadPlpResult.html index 2bc94b591..25327ba94 100644 --- a/reference/loadPlpResult.html +++ b/reference/loadPlpResult.html @@ -1,9 +1,9 @@ -Loads the evalaution dataframe — loadPlpResult • PatientLevelPredictionLoads the evalaution dataframe — loadPlpResult • PatientLevelPrediction - +
      - +

      Loads the evalaution dataframe

      - Source: R/SaveLoadPlp.R + Source: R/SaveLoadPlp.R
      @@ -137,9 +137,7 @@

      Loads the evalaution dataframe

      Arguments

      - - -
      dirPath
      +
      dirPath

      The directory where the evaluation was saved

      @@ -160,15 +158,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/loadPlpShareable.html b/reference/loadPlpShareable.html index 38d0d667b..a69479f3d 100644 --- a/reference/loadPlpShareable.html +++ b/reference/loadPlpShareable.html @@ -1,9 +1,9 @@ -Loads the plp result saved as json/csv files for transparent sharing — loadPlpShareable • PatientLevelPredictionLoads the plp result saved as json/csv files for transparent sharing — loadPlpShareable • PatientLevelPrediction - +
      - +

      Loads the plp result saved as json/csv files for transparent sharing

      - Source: R/SaveLoadPlp.R + Source: R/SaveLoadPlp.R
      @@ -137,9 +137,7 @@

      Loads the plp result saved as json/csv files for transparent sharing

      Arguments

      - - -
      loadDirectory
      +
      loadDirectory

      The directory with the results as json/csv files

      @@ -160,15 +158,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/loadPrediction.html b/reference/loadPrediction.html index 85dc3d834..a4715f9c5 100644 --- a/reference/loadPrediction.html +++ b/reference/loadPrediction.html @@ -1,9 +1,9 @@ -Loads the prediciton dataframe to csv — loadPrediction • PatientLevelPredictionLoads the prediciton dataframe to csv — loadPrediction • PatientLevelPrediction - +
      - +

      Loads the prediciton dataframe to csv

      - Source: R/SaveLoadPlp.R + Source: R/SaveLoadPlp.R
      @@ -137,9 +137,7 @@

      Loads the prediciton dataframe to csv

      Arguments

      - - -
      fileLocation
      +
      fileLocation

      The location with the saved prediction

      @@ -160,15 +158,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/migrateDataModel.html b/reference/migrateDataModel.html index fcbd82fa7..5810fedca 100644 --- a/reference/migrateDataModel.html +++ b/reference/migrateDataModel.html @@ -1,11 +1,11 @@ -Migrate Data model — migrateDataModel • PatientLevelPredictionMigrate Data model — migrateDataModel • PatientLevelPrediction - +
      - +

      Migrate Data model

      - Source: R/DatabaseMigration.R + Source: R/DatabaseMigration.R
      @@ -141,17 +141,15 @@

      Migrate Data model

      Arguments

      - - -
      connectionDetails
      +
      connectionDetails

      DatabaseConnector connection details object

      -
      databaseSchema
      +
      databaseSchema

      String schema where database schema lives

      -
      tablePrefix
      +
      tablePrefix

      (Optional) Use if a table prefix is used before table names (e.g. "cd_")

      @@ -168,15 +166,15 @@

      Arguments

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/modelBasedConcordance.html b/reference/modelBasedConcordance.html index dafc5984c..b18d502df 100644 --- a/reference/modelBasedConcordance.html +++ b/reference/modelBasedConcordance.html @@ -1,10 +1,11 @@ -Calculate the model-based concordance, which is a calculation of the expected discrimination performance of a model under the assumption the model predicts the "TRUE" outcome as detailed in van Klaveren et al. https://pubmed.ncbi.nlm.nih.gov/27251001/ — modelBasedConcordance • PatientLevelPrediction - +
      - +
      -

      Calculate the model-based concordance, which is a calculation of the expected discrimination performance of a model under the assumption the model predicts the "TRUE" outcome as detailed in van Klaveren et al. https://pubmed.ncbi.nlm.nih.gov/27251001/

      - Source: R/EvaluatePlp.R +

      Calculate the model-based concordance, which is a calculation of the expected discrimination performance of a model under the assumption the model predicts the "TRUE" outcome +as detailed in van Klaveren et al. https://pubmed.ncbi.nlm.nih.gov/27251001/

      + Source: R/EvaluatePlp.R
      @@ -139,15 +141,15 @@

      Calculate the model-based concordance, which is a calculation of the expecte

      Arguments

      - - -
      prediction
      +
      prediction

      the prediction object found in the plpResult object

      Value

      -

      model-based concordance value

      + + +

      model-based concordance value

      Details

      @@ -166,15 +168,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/negativeLikelihoodRatio.html b/reference/negativeLikelihoodRatio.html index 7de34baf1..a1fa5b4f2 100644 --- a/reference/negativeLikelihoodRatio.html +++ b/reference/negativeLikelihoodRatio.html @@ -1,9 +1,9 @@ -Calculate the negativeLikelihoodRatio — negativeLikelihoodRatio • PatientLevelPredictionCalculate the negativeLikelihoodRatio — negativeLikelihoodRatio • PatientLevelPrediction - +
      - +

      Calculate the negativeLikelihoodRatio

      - Source: R/ThresholdSummary.R + Source: R/ThresholdSummary.R
      @@ -137,27 +137,27 @@

      Calculate the negativeLikelihoodRatio

      Arguments

      - - -
      TP
      +
      TP

      Number of true positives

      -
      TN
      +
      TN

      Number of true negatives

      -
      FN
      +
      FN

      Number of false negatives

      -
      FP
      +
      FP

      Number of false positives

      Value

      -

      negativeLikelihoodRatio value

      + + +

      negativeLikelihoodRatio value

      Details

      @@ -176,15 +176,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/negativePredictiveValue.html b/reference/negativePredictiveValue.html index e26dd196a..816870bb3 100644 --- a/reference/negativePredictiveValue.html +++ b/reference/negativePredictiveValue.html @@ -1,9 +1,9 @@ -Calculate the negativePredictiveValue — negativePredictiveValue • PatientLevelPredictionCalculate the negativePredictiveValue — negativePredictiveValue • PatientLevelPrediction - +
      - +

      Calculate the negativePredictiveValue

      - Source: R/ThresholdSummary.R + Source: R/ThresholdSummary.R
      @@ -137,27 +137,27 @@

      Calculate the negativePredictiveValue

      Arguments

      - - -
      TP
      +
      TP

      Number of true positives

      -
      TN
      +
      TN

      Number of true negatives

      -
      FN
      +
      FN

      Number of false negatives

      -
      FP
      +
      FP

      Number of false positives

      Value

      -

      negativePredictiveValue value

      + + +

      negativePredictiveValue value

      Details

      @@ -176,15 +176,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/outcomeSurvivalPlot.html b/reference/outcomeSurvivalPlot.html index bbfe0da03..fc364c7e5 100644 --- a/reference/outcomeSurvivalPlot.html +++ b/reference/outcomeSurvivalPlot.html @@ -1,9 +1,9 @@ -Plot the outcome incidence over time — outcomeSurvivalPlot • PatientLevelPredictionPlot the outcome incidence over time — outcomeSurvivalPlot • PatientLevelPrediction - +
      - +

      Plot the outcome incidence over time

      - Source: R/Plotting.R + Source: R/Plotting.R
      @@ -147,35 +147,35 @@

      Plot the outcome incidence over time

      Arguments

      - - -
      plpData
      +
      plpData

      The plpData object returned by running getPlpData()

      -
      outcomeId
      +
      outcomeId

      The cohort id corresponding to the outcome

      -
      populationSettings
      +
      populationSettings

      The population settings created using createStudyPopulationSettings

      -
      riskTable
      +
      riskTable

      (binary) Whether to include a table at the bottom of the plot showing the number of people at risk over time

      -
      confInt
      +
      confInt

      (binary) Whether to include a confidence interval

      -
      yLabel
      +
      yLabel

      (string) The label for the y-axis

      Value

      -

      TRUE if it ran

      + + +

      TRUE if it ran

      Details

      @@ -194,15 +194,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/pfi.html b/reference/pfi.html index c9c307dd2..8a039c50a 100644 --- a/reference/pfi.html +++ b/reference/pfi.html @@ -1,9 +1,9 @@ -pfi — pfi • PatientLevelPredictionpfi — pfi • PatientLevelPrediction - +
      - +

      pfi

      - Source: R/FeatureImportance.R + Source: R/FeatureImportance.R
      @@ -146,44 +146,44 @@

      pfi

      Arguments

      - - -
      plpResult
      +
      plpResult

      An object of type runPlp

      -
      population
      +
      population

      The population created using createStudyPopulation() who will have their risks predicted

      -
      plpData
      +
      plpData

      An object of type plpData - the patient level prediction data extracted from the CDM.

      -
      repeats
      +
      repeats

      The number of times to permute each covariate

      -
      covariates
      +
      covariates

      A vector of covariates to calculate the pfi for. If NULL it uses all covariates included in the model.

      -
      cores
      +
      cores

      Number of cores to use when running this (it runs in parallel)

      -
      log
      +
      log

      A location to save the log for running pfi

      -
      logthreshold
      +
      logthreshold

      The log threshold (e.g., INFO, TRACE, ...)

      Value

      -

      A dataframe with the covariateIds and the pfi (change in AUC caused by permuting the covariate) value

      + + +

      A dataframe with the covariateIds and the pfi (change in AUC caused by permuting the covariate) value

      Details

      @@ -202,15 +202,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/plotDemographicSummary.html b/reference/plotDemographicSummary.html index a7dd67516..8756040c8 100644 --- a/reference/plotDemographicSummary.html +++ b/reference/plotDemographicSummary.html @@ -1,9 +1,9 @@ -Plot the Observed vs. expected incidence, by age and gender — plotDemographicSummary • PatientLevelPredictionPlot the Observed vs. expected incidence, by age and gender — plotDemographicSummary • PatientLevelPrediction - +
      - +

      Plot the Observed vs. expected incidence, by age and gender

      - Source: R/Plotting.R + Source: R/Plotting.R
      @@ -142,21 +142,19 @@

      Plot the Observed vs. expected incidence, by age and gender

      Arguments

      - - -
      plpResult
      +
      plpResult

      A plp result object as generated using the runPlp function.

      -
      typeColumn
      +
      typeColumn

      The name of the column specifying the evaluation type

      -
      saveLocation
      +
      saveLocation

      Directory to save plot (if NULL plot is not saved)

      -
      fileName
      +
      fileName

      Name of the file to save to plot, for example 'plot.png'. See the function ggsave in the ggplot2 package for supported file formats.

      @@ -164,7 +162,9 @@

      Arguments

      Value

      -

      A ggplot object. Use the ggsave function to save to file in a different + + +

      A ggplot object. Use the ggsave function to save to file in a different format.

      @@ -185,15 +185,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/plotF1Measure.html b/reference/plotF1Measure.html index 906f3a7b2..aec0a7eae 100644 --- a/reference/plotF1Measure.html +++ b/reference/plotF1Measure.html @@ -1,9 +1,9 @@ -Plot the F1 measure efficiency frontier using the sparse thresholdSummary data frame — plotF1Measure • PatientLevelPredictionPlot the F1 measure efficiency frontier using the sparse thresholdSummary data frame — plotF1Measure • PatientLevelPrediction - +
      - +

      Plot the F1 measure efficiency frontier using the sparse thresholdSummary data frame

      - Source: R/Plotting.R + Source: R/Plotting.R
      @@ -142,21 +142,19 @@

      Plot the F1 measure efficiency frontier using the sparse thresholdSummary da

      Arguments

      - - -
      plpResult
      +
      plpResult

      A plp result object as generated using the runPlp function.

      -
      typeColumn
      +
      typeColumn

      The name of the column specifying the evaluation type

      -
      saveLocation
      +
      saveLocation

      Directory to save plot (if NULL plot is not saved)

      -
      fileName
      +
      fileName

      Name of the file to save to plot, for example 'plot.png'. See the function ggsave in the ggplot2 package for supported file formats.

      @@ -164,7 +162,9 @@

      Arguments

      Value

      -

      A ggplot object. Use the ggsave function to save to file in a different + + +

      A ggplot object. Use the ggsave function to save to file in a different format.

      @@ -184,15 +184,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/plotGeneralizability.html b/reference/plotGeneralizability.html index 0aa8bdc8c..f91a77613 100644 --- a/reference/plotGeneralizability.html +++ b/reference/plotGeneralizability.html @@ -1,9 +1,9 @@ -Plot the train/test generalizability diagnostic — plotGeneralizability • PatientLevelPredictionPlot the train/test generalizability diagnostic — plotGeneralizability • PatientLevelPrediction - +
      - +

      Plot the train/test generalizability diagnostic

      - Source: R/Plotting.R + Source: R/Plotting.R
      @@ -141,18 +141,16 @@

      Plot the train/test generalizability diagnostic

      Arguments

      - - -
      covariateSummary
      +
      covariateSummary

      A prediction object as generated using the runPlp function.

      -
      saveLocation
      +
      saveLocation

      Directory to save plot (if NULL plot is not saved)

      -
      fileName
      +
      fileName

      Name of the file to save to plot, for example 'plot.png'. See the function ggsave in the ggplot2 package for supported file formats.

      @@ -160,7 +158,9 @@

      Arguments

      Value

      -

      A ggplot object. Use the ggsave function to save to file in a different + + +

      A ggplot object. Use the ggsave function to save to file in a different format.

      @@ -181,15 +181,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/plotLearningCurve.html b/reference/plotLearningCurve.html index 742a1d8a8..803ab6e4f 100644 --- a/reference/plotLearningCurve.html +++ b/reference/plotLearningCurve.html @@ -1,10 +1,10 @@ -plotLearningCurve — plotLearningCurve • PatientLevelPredictionplotLearningCurve — plotLearningCurve • PatientLevelPrediction - +
      - +

      plotLearningCurve

      - Source: R/LearningCurve.R + Source: R/LearningCurve.R
      @@ -146,14 +146,12 @@

      plotLearningCurve

      Arguments

      - - -
      learningCurve
      +
      learningCurve

      An object returned by createLearningCurve function.

      -
      metric
      +
      metric

      Specifies the metric to be plotted:

      • 'AUROC' - use the area under the Receiver Operating Characteristic curve

      • 'AUPRC' - use the area under the Precision-Recall curve

        • @@ -161,42 +159,44 @@

        Arguments

      -
      abscissa
      +
      abscissa

      Specify the abscissa metric to be plotted:

      • 'events' - use number of events

      • 'observations' - use number of observations

      -
      plotTitle
      +
      plotTitle

      Title of the learning curve plot.

      -
      plotSubtitle
      +
      plotSubtitle

      Subtitle of the learning curve plot.

      -
      fileName
      +
      fileName

      Filename of plot to be saved, for example 'plot.png'. -See the function ggsave in the ggplot2 package for supported file +See the function ggsave in the ggplot2 package for supported file formats.

      Value

      -

      A ggplot object. Use the ggsave function to save to + + +

      A ggplot object. Use the ggsave function to save to file in a different format.

      Examples

      - 
      if (FALSE) { # \dontrun{
+    
      if (FALSE) {
 # create learning curve object
 learningCurve <- createLearningCurve(population,
                                      plpData,
                                      modelSettings)
 # plot the learning curve
 plotLearningCurve(learningCurve)
-} # }
+}
      @@ -212,15 +212,15 @@

      Examples

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/plotPlp.html b/reference/plotPlp.html index c9d7fa0ea..0ae53ad83 100644 --- a/reference/plotPlp.html +++ b/reference/plotPlp.html @@ -1,9 +1,9 @@ -Plot all the PatientLevelPrediction plots — plotPlp • PatientLevelPredictionPlot all the PatientLevelPrediction plots — plotPlp • PatientLevelPrediction - +
      - +

      Plot all the PatientLevelPrediction plots

      - Source: R/Plotting.R + Source: R/Plotting.R
      @@ -137,24 +137,24 @@

      Plot all the PatientLevelPrediction plots

      Arguments

      - - -
      plpResult
      +
      plpResult

      Object returned by the runPlp() function

      -
      saveLocation
      +
      saveLocation

      Name of the directory where the plots should be saved (NULL means no saving)

      -
      typeColumn
      -

      The name of the column specifying the evaluation type +

      typeColumn
      +

      The name of the column specifying the evaluation type (to stratify the plots)

      Value

      -

      TRUE if it ran

      + + +

      TRUE if it ran

      Details

      @@ -173,15 +173,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/plotPrecisionRecall.html b/reference/plotPrecisionRecall.html index 322006494..9296f567e 100644 --- a/reference/plotPrecisionRecall.html +++ b/reference/plotPrecisionRecall.html @@ -1,9 +1,9 @@ -Plot the precision-recall curve using the sparse thresholdSummary data frame — plotPrecisionRecall • PatientLevelPredictionPlot the precision-recall curve using the sparse thresholdSummary data frame — plotPrecisionRecall • PatientLevelPrediction - +
      - +

      Plot the precision-recall curve using the sparse thresholdSummary data frame

      - Source: R/Plotting.R + Source: R/Plotting.R
      @@ -142,21 +142,19 @@

      Plot the precision-recall curve using the sparse thresholdSummary data frame

      Arguments

      - - -
      plpResult
      +
      plpResult

      A plp result object as generated using the runPlp function.

      -
      typeColumn
      +
      typeColumn

      The name of the column specifying the evaluation type

      -
      saveLocation
      +
      saveLocation

      Directory to save plot (if NULL plot is not saved)

      -
      fileName
      +
      fileName

      Name of the file to save to plot, for example 'plot.png'. See the function ggsave in the ggplot2 package for supported file formats.

      @@ -164,7 +162,9 @@

      Arguments

      Value

      -

      A ggplot object. Use the ggsave function to save to file in a different + + +

      A ggplot object. Use the ggsave function to save to file in a different format.

      @@ -184,15 +184,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/plotPredictedPDF.html b/reference/plotPredictedPDF.html index ea17154c9..f95ad0bc3 100644 --- a/reference/plotPredictedPDF.html +++ b/reference/plotPredictedPDF.html @@ -1,9 +1,9 @@ -Plot the Predicted probability density function, showing prediction overlap between true and false cases — plotPredictedPDF • PatientLevelPredictionPlot the Predicted probability density function, showing prediction overlap between true and false cases — plotPredictedPDF • PatientLevelPrediction - +
      - +

      Plot the Predicted probability density function, showing prediction overlap between true and false cases

      - Source: R/Plotting.R + Source: R/Plotting.R
      @@ -142,21 +142,19 @@

      Plot the Predicted probability density function, showing prediction overlap

      Arguments

      - - -
      plpResult
      +
      plpResult

      A plp result object as generated using the runPlp function.

      -
      typeColumn
      +
      typeColumn

      The name of the column specifying the evaluation type

      -
      saveLocation
      +
      saveLocation

      Directory to save plot (if NULL plot is not saved)

      -
      fileName
      +
      fileName

      Name of the file to save to plot, for example 'plot.png'. See the function ggsave in the ggplot2 package for supported file formats.

      @@ -164,7 +162,9 @@

      Arguments

      Value

      -

      A ggplot object. Use the ggsave function to save to file in a different + + +

      A ggplot object. Use the ggsave function to save to file in a different format.

      @@ -184,15 +184,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/plotPredictionDistribution.html b/reference/plotPredictionDistribution.html index 8e9dab403..f39445b5c 100644 --- a/reference/plotPredictionDistribution.html +++ b/reference/plotPredictionDistribution.html @@ -1,9 +1,9 @@ -Plot the side-by-side boxplots of prediction distribution, by class#' — plotPredictionDistribution • PatientLevelPredictionPlot the side-by-side boxplots of prediction distribution, by class#' — plotPredictionDistribution • PatientLevelPrediction - +
      - +

      Plot the side-by-side boxplots of prediction distribution, by class#'

      - Source: R/Plotting.R + Source: R/Plotting.R
      @@ -142,21 +142,19 @@

      Plot the side-by-side boxplots of prediction distribution, by class#'

      Arguments

      - - -
      plpResult
      +
      plpResult

      A plp result object as generated using the runPlp function.

      -
      typeColumn
      +
      typeColumn

      The name of the column specifying the evaluation type

      -
      saveLocation
      +
      saveLocation

      Directory to save plot (if NULL plot is not saved)

      -
      fileName
      +
      fileName

      Name of the file to save to plot, for example 'plot.png'. See the function ggsave in the ggplot2 package for supported file formats.

      @@ -164,7 +162,9 @@

      Arguments

      Value

      -

      A ggplot object. Use the ggsave function to save to file in a different + + +

      A ggplot object. Use the ggsave function to save to file in a different format.

      @@ -185,15 +185,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/plotPreferencePDF.html b/reference/plotPreferencePDF.html index 111af7230..8b26d68a7 100644 --- a/reference/plotPreferencePDF.html +++ b/reference/plotPreferencePDF.html @@ -1,10 +1,12 @@ -Plot the preference score probability density function, showing prediction overlap between true and false cases #' — plotPreferencePDF • PatientLevelPredictionPlot the preference score probability density function, showing prediction overlap between true and false cases +#' — plotPreferencePDF • PatientLevelPrediction - +
      - +
      -

      Plot the preference score probability density function, showing prediction overlap between true and false cases #'

      - Source: R/Plotting.R +

      Plot the preference score probability density function, showing prediction overlap between true and false cases +#'

      + Source: R/Plotting.R
      @@ -144,21 +147,19 @@

      Plot the preference score probability density function, showing prediction o

      Arguments

      - - -
      plpResult
      +
      plpResult

      A plp result object as generated using the runPlp function.

      -
      typeColumn
      +
      typeColumn

      The name of the column specifying the evaluation type

      -
      saveLocation
      +
      saveLocation

      Directory to save plot (if NULL plot is not saved)

      -
      fileName
      +
      fileName

      Name of the file to save to plot, for example 'plot.png'. See the function ggsave in the ggplot2 package for supported file formats.

      @@ -166,7 +167,9 @@

      Arguments

      Value

      -

      A ggplot object. Use the ggsave function to save to file in a different + + +

      A ggplot object. Use the ggsave function to save to file in a different format.

      @@ -187,15 +190,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/plotSmoothCalibration.html b/reference/plotSmoothCalibration.html index cff578ce7..bb6bd7cd4 100644 --- a/reference/plotSmoothCalibration.html +++ b/reference/plotSmoothCalibration.html @@ -1,10 +1,11 @@ -Plot the smooth calibration as detailed in Calster et al. "A calibration heirarchy for risk models was defined: from utopia to empirical data" (2016) — plotSmoothCalibration • PatientLevelPrediction - +
      - +
      -

      Plot the smooth calibration as detailed in Calster et al. "A calibration heirarchy for risk models was defined: from utopia to empirical data" (2016)

      - Source: R/Plotting.R +

      Plot the smooth calibration as detailed in Calster et al. "A calibration heirarchy for risk models +was defined: from utopia to empirical data" (2016)

      + Source: R/Plotting.R
      @@ -150,49 +152,47 @@

      Plot the smooth calibration as detailed in Calster et al. "A calibration hei

      Arguments

      - - -
      plpResult
      +
      plpResult

      The result of running runPlp function. An object containing the model or location where the model is save, the data selection settings, the preprocessing and training settings as well as various performance measures obtained by the model.

      -
      smooth
      +
      smooth

      options: 'loess' or 'rcs'

      -
      span
      +
      span

      This specifies the width of span used for loess. This will allow for faster computing and lower memory usage.

      -
      nKnots
      +
      nKnots

      The number of knots to be used by the rcs evaluation. Default is 5

      -
      scatter
      +
      scatter

      plot the decile calibrations as points on the graph. Default is False

      -
      bins
      +
      bins

      The number of bins for the histogram. Default is 20.

      -
      sample
      +
      sample

      If using loess then by default 20,000 patients will be sampled to save time

      -
      typeColumn
      +
      typeColumn

      The name of the column specifying the evaluation type

      -
      saveLocation
      +
      saveLocation

      Directory to save plot (if NULL plot is not saved)

      -
      fileName
      +
      fileName

      Name of the file to save to plot, for example 'plot.png'. See the function ggsave in the ggplot2 package for supported file formats.

      @@ -200,7 +200,9 @@

      Arguments

      Value

      -

      A ggplot object.

      + + +

      A ggplot object.

      Details

      @@ -219,15 +221,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/plotSparseCalibration.html b/reference/plotSparseCalibration.html index 245537683..cb1bc73d3 100644 --- a/reference/plotSparseCalibration.html +++ b/reference/plotSparseCalibration.html @@ -1,9 +1,9 @@ -Plot the calibration — plotSparseCalibration • PatientLevelPredictionPlot the calibration — plotSparseCalibration • PatientLevelPrediction - +
      - +

      Plot the calibration

      - Source: R/Plotting.R + Source: R/Plotting.R
      @@ -142,21 +142,19 @@

      Plot the calibration

      Arguments

      - - -
      plpResult
      +
      plpResult

      A plp result object as generated using the runPlp function.

      -
      typeColumn
      +
      typeColumn

      The name of the column specifying the evaluation type

      -
      saveLocation
      +
      saveLocation

      Directory to save plot (if NULL plot is not saved)

      -
      fileName
      +
      fileName

      Name of the file to save to plot, for example 'plot.png'. See the function ggsave in the ggplot2 package for supported file formats.

      @@ -164,7 +162,9 @@

      Arguments

      Value

      -

      A ggplot object. Use the ggsave function to save to file in a different + + +

      A ggplot object. Use the ggsave function to save to file in a different format.

      @@ -185,15 +185,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/plotSparseCalibration2.html b/reference/plotSparseCalibration2.html index 62bf08fc7..34929c48a 100644 --- a/reference/plotSparseCalibration2.html +++ b/reference/plotSparseCalibration2.html @@ -1,9 +1,9 @@ -Plot the conventional calibration — plotSparseCalibration2 • PatientLevelPredictionPlot the conventional calibration — plotSparseCalibration2 • PatientLevelPrediction - +
      - +

      Plot the conventional calibration

      - Source: R/Plotting.R + Source: R/Plotting.R
      @@ -142,21 +142,19 @@

      Plot the conventional calibration

      Arguments

      - - -
      plpResult
      +
      plpResult

      A plp result object as generated using the runPlp function.

      -
      typeColumn
      +
      typeColumn

      The name of the column specifying the evaluation type

      -
      saveLocation
      +
      saveLocation

      Directory to save plot (if NULL plot is not saved)

      -
      fileName
      +
      fileName

      Name of the file to save to plot, for example 'plot.png'. See the function ggsave in the ggplot2 package for supported file formats.

      @@ -164,7 +162,9 @@

      Arguments

      Value

      -

      A ggplot object. Use the ggsave function to save to file in a different + + +

      A ggplot object. Use the ggsave function to save to file in a different format.

      @@ -185,15 +185,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/plotSparseRoc.html b/reference/plotSparseRoc.html index c5da332bf..37ef3c135 100644 --- a/reference/plotSparseRoc.html +++ b/reference/plotSparseRoc.html @@ -1,9 +1,9 @@ -Plot the ROC curve using the sparse thresholdSummary data frame — plotSparseRoc • PatientLevelPredictionPlot the ROC curve using the sparse thresholdSummary data frame — plotSparseRoc • PatientLevelPrediction - +
      - +

      Plot the ROC curve using the sparse thresholdSummary data frame

      - Source: R/Plotting.R + Source: R/Plotting.R
      @@ -142,21 +142,19 @@

      Plot the ROC curve using the sparse thresholdSummary data frame

      Arguments

      - - -
      plpResult
      +
      plpResult

      A plp result object as generated using the runPlp function.

      -
      typeColumn
      +
      typeColumn

      The name of the column specifying the evaluation type

      -
      saveLocation
      +
      saveLocation

      Directory to save plot (if NULL plot is not saved)

      -
      fileName
      +
      fileName

      Name of the file to save to plot, for example 'plot.png'. See the function ggsave in the ggplot2 package for supported file formats.

      @@ -164,7 +162,9 @@

      Arguments

      Value

      -

      A ggplot object. Use the ggsave function to save to file in a different + + +

      A ggplot object. Use the ggsave function to save to file in a different format.

      @@ -184,15 +184,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/plotVariableScatterplot.html b/reference/plotVariableScatterplot.html index 256c6ddd2..752c4e840 100644 --- a/reference/plotVariableScatterplot.html +++ b/reference/plotVariableScatterplot.html @@ -1,9 +1,9 @@ -Plot the variable importance scatterplot — plotVariableScatterplot • PatientLevelPredictionPlot the variable importance scatterplot — plotVariableScatterplot • PatientLevelPrediction - +
      - +

      Plot the variable importance scatterplot

      - Source: R/Plotting.R + Source: R/Plotting.R
      @@ -141,18 +141,16 @@

      Plot the variable importance scatterplot

      Arguments

      - - -
      covariateSummary
      +
      covariateSummary

      A prediction object as generated using the runPlp function.

      -
      saveLocation
      +
      saveLocation

      Directory to save plot (if NULL plot is not saved)

      -
      fileName
      +
      fileName

      Name of the file to save to plot, for example 'plot.png'. See the function ggsave in the ggplot2 package for supported file formats.

      @@ -160,7 +158,9 @@

      Arguments

      Value

      -

      A ggplot object. Use the ggsave function to save to file in a different + + +

      A ggplot object. Use the ggsave function to save to file in a different format.

      @@ -181,15 +181,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/plpDataSimulationProfile.html b/reference/plpDataSimulationProfile.html index be4a39f97..cdf677259 100644 --- a/reference/plpDataSimulationProfile.html +++ b/reference/plpDataSimulationProfile.html @@ -1,9 +1,9 @@ -A simulation profile — plpDataSimulationProfile • PatientLevelPredictionA simulation profile — plpDataSimulationProfile • PatientLevelPrediction - +
      - +

      A simulation profile

      - Source: R/PatientLevelPrediction.R + Source: R/PatientLevelPrediction.R
      @@ -170,15 +170,15 @@

      Format

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/positiveLikelihoodRatio.html b/reference/positiveLikelihoodRatio.html index e4d8595e8..15d6fb14f 100644 --- a/reference/positiveLikelihoodRatio.html +++ b/reference/positiveLikelihoodRatio.html @@ -1,9 +1,9 @@ -Calculate the positiveLikelihoodRatio — positiveLikelihoodRatio • PatientLevelPredictionCalculate the positiveLikelihoodRatio — positiveLikelihoodRatio • PatientLevelPrediction - +
      - +

      Calculate the positiveLikelihoodRatio

      - Source: R/ThresholdSummary.R + Source: R/ThresholdSummary.R
      @@ -137,27 +137,27 @@

      Calculate the positiveLikelihoodRatio

      Arguments

      - - -
      TP
      +
      TP

      Number of true positives

      -
      TN
      +
      TN

      Number of true negatives

      -
      FN
      +
      FN

      Number of false negatives

      -
      FP
      +
      FP

      Number of false positives

      Value

      -

      positiveLikelihoodRatio value

      + + +

      positiveLikelihoodRatio value

      Details

      @@ -176,15 +176,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/positivePredictiveValue.html b/reference/positivePredictiveValue.html index 7143da7f4..8e32622a2 100644 --- a/reference/positivePredictiveValue.html +++ b/reference/positivePredictiveValue.html @@ -1,9 +1,9 @@ -Calculate the positivePredictiveValue — positivePredictiveValue • PatientLevelPredictionCalculate the positivePredictiveValue — positivePredictiveValue • PatientLevelPrediction - +
      - +

      Calculate the positivePredictiveValue

      - Source: R/ThresholdSummary.R + Source: R/ThresholdSummary.R
      @@ -137,27 +137,27 @@

      Calculate the positivePredictiveValue

      Arguments

      - - -
      TP
      +
      TP

      Number of true positives

      -
      TN
      +
      TN

      Number of true negatives

      -
      FN
      +
      FN

      Number of false negatives

      -
      FP
      +
      FP

      Number of false positives

      Value

      -

      positivePredictiveValue value

      + + +

      positivePredictiveValue value

      Details

      @@ -176,15 +176,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/predictCyclops.html b/reference/predictCyclops.html index 1fb9324bf..8482299c3 100644 --- a/reference/predictCyclops.html +++ b/reference/predictCyclops.html @@ -1,9 +1,9 @@ -Create predictive probabilities — predictCyclops • PatientLevelPredictionCreate predictive probabilities — predictCyclops • PatientLevelPrediction - +
      - +

      Create predictive probabilities

      - Source: R/CyclopsModels.R + Source: R/CyclopsModels.R
      @@ -137,24 +137,24 @@

      Create predictive probabilities

      Arguments

      - - -
      plpModel
      +
      plpModel

      An object of type predictiveModel as generated using fitPlp.

      -
      data
      +
      data

      The new plpData containing the covariateData for the new population

      -
      cohort
      +
      cohort

      The cohort to calculate the prediction for

      Value

      -

      The value column in the result data.frame is: logistic: probabilities of the outcome, poisson: + + +

      The value column in the result data.frame is: logistic: probabilities of the outcome, poisson: Poisson rate (per day) of the outome, survival: hazard rate (per day) of the outcome.

      @@ -174,15 +174,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/predictPlp.html b/reference/predictPlp.html index b9f065195..1765cf0ec 100644 --- a/reference/predictPlp.html +++ b/reference/predictPlp.html @@ -1,9 +1,9 @@ -predictPlp — predictPlp • PatientLevelPredictionpredictPlp — predictPlp • PatientLevelPrediction - +
      - +

      predictPlp

      - Source: R/Predict.R + Source: R/Predict.R
      @@ -137,28 +137,28 @@

      predictPlp

      Arguments

      - - -
      plpModel
      +
      plpModel

      An object of type plpModel - a patient level prediction model

      -
      plpData
      +
      plpData

      An object of type plpData - the patient level prediction data extracted from the CDM.

      -
      population
      +
      population

      The population created using createStudyPopulation() who will have their risks predicted or a cohort without the outcome known

      -
      timepoint
      +
      timepoint

      The timepoint to predict risk (survival models only)

      Value

      -

      A dataframe containing the prediction for each person in the population with an attribute metaData containing prediction details.

      + + +

      A dataframe containing the prediction for each person in the population with an attribute metaData containing prediction details.

      Details

      @@ -177,15 +177,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/preprocessData.html b/reference/preprocessData.html index 26504be35..c8c042505 100644 --- a/reference/preprocessData.html +++ b/reference/preprocessData.html @@ -1,10 +1,12 @@ -A function that wraps around FeatureExtraction::tidyCovariateData to normalise the data and remove rare or redundant features — preprocessData • PatientLevelPredictionA function that wraps around FeatureExtraction::tidyCovariateData to normalise the data +and remove rare or redundant features — preprocessData • PatientLevelPrediction - +
      - +
      -

      A function that wraps around FeatureExtraction::tidyCovariateData to normalise the data and remove rare or redundant features

      - Source: R/PreprocessingData.R +

      A function that wraps around FeatureExtraction::tidyCovariateData to normalise the data +and remove rare or redundant features

      + Source: R/PreprocessingData.R
      @@ -139,20 +142,20 @@

      A function that wraps around FeatureExtraction::tidyCovariateData to normali

      Arguments

      - - -
      covariateData
      -

      The covariate part of the training data created by splitData after being sampled and having +

      covariateData
      +

      The covariate part of the training data created by splitData after being sampled and having any required feature engineering

      -
      preprocessSettings
      +
      preprocessSettings

      The settings for the preprocessing created by createPreprocessSettings

      Value

      -

      The data processed

      + + +

      The data processed

      Details

      @@ -171,15 +174,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/recalibratePlp.html b/reference/recalibratePlp.html index d6b5b6960..d9fb6af46 100644 --- a/reference/recalibratePlp.html +++ b/reference/recalibratePlp.html @@ -1,9 +1,9 @@ -recalibratePlp — recalibratePlp • PatientLevelPredictionrecalibratePlp — recalibratePlp • PatientLevelPrediction - +
      - +

      recalibratePlp

      - Source: R/Recalibration.R + Source: R/Recalibration.R
      @@ -142,27 +142,27 @@

      recalibratePlp

      Arguments

      - - -
      prediction
      +
      prediction

      A prediction dataframe

      -
      analysisId
      +
      analysisId

      The model analysisId

      -
      typeColumn
      +
      typeColumn

      The column name where the strata types are specified

      -
      method
      +
      method

      Method used to recalibrate ('recalibrationInTheLarge' or 'weakRecalibration' )

      Value

      -

      An object of class runPlp that is recalibrated on the new data

      + + +

      An object of class runPlp that is recalibrated on the new data

      Details

      @@ -182,15 +182,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/recalibratePlpRefit.html b/reference/recalibratePlpRefit.html index 7d21373c0..f9338fe9b 100644 --- a/reference/recalibratePlpRefit.html +++ b/reference/recalibratePlpRefit.html @@ -1,9 +1,9 @@ -recalibratePlpRefit — recalibratePlpRefit • PatientLevelPredictionrecalibratePlpRefit — recalibratePlpRefit • PatientLevelPrediction - +
      - +

      recalibratePlpRefit

      - Source: R/Recalibration.R + Source: R/Recalibration.R
      @@ -137,24 +137,24 @@

      recalibratePlpRefit

      Arguments

      - - -
      plpModel
      +
      plpModel

      The trained plpModel (runPlp$model)

      -
      newPopulation
      +
      newPopulation

      The population created using createStudyPopulation() who will have their risks predicted

      -
      newData
      +
      newData

      An object of type plpData - the patient level prediction data extracted from the CDM.

      Value

      -

      An object of class runPlp that is recalibrated on the new data

      + + +

      An object of class runPlp that is recalibrated on the new data

      Details

      @@ -174,15 +174,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/runMultiplePlp.html b/reference/runMultiplePlp.html index bfe57c3a7..5e36a1c9f 100644 --- a/reference/runMultiplePlp.html +++ b/reference/runMultiplePlp.html @@ -1,9 +1,9 @@ -Run a list of predictions analyses — runMultiplePlp • PatientLevelPredictionRun a list of predictions analyses — runMultiplePlp • PatientLevelPrediction - +
      - +

      Run a list of predictions analyses

      - Source: R/RunMultiplePlp.R + Source: R/RunMultiplePlp.R
      @@ -148,39 +148,39 @@

      Run a list of predictions analyses

      Arguments

      - - -
      databaseDetails
      +
      databaseDetails

      The database settings created using createDatabaseDetails()

      -
      modelDesignList
      +
      modelDesignList

      A list of model designs created using createModelDesign()

      -
      onlyFetchData
      +
      onlyFetchData

      Only fetches and saves the data object to the output folder without running the analysis.

      -
      cohortDefinitions
      +
      cohortDefinitions

      A list of cohort definitions for the target and outcome cohorts

      -
      logSettings
      +
      logSettings

      The setting specifying the logging for the analyses created using createLogSettings()

      -
      saveDirectory
      +
      saveDirectory

      Name of the folder where all the outputs will written to.

      -
      sqliteLocation
      +
      sqliteLocation

      (optional) The location of the sqlite database with the results

      Value

      -

      A data frame with the following columns:

      analysisIdThe unique identifier + + +

      A data frame with the following columns:

      analysisIdThe unique identifier for a set of analysis choices.
      targetIdThe ID of the target cohort populations.
      outcomeIdThe ID of the outcomeId.
      dataLocationThe location where the plpData was saved
      the settings idsThe ids for all other settings used for model development.

      Details

      @@ -189,15 +189,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/diagnosePlp.html b/reference/diagnosePlp.html index 9030cfa87..f4282a426 100644 --- a/reference/diagnosePlp.html +++ b/reference/diagnosePlp.html @@ -1,10 +1,10 @@ -diagnostic - Investigates the prediction problem settings - use before training a model — diagnosePlp • PatientLevelPredictiondiagnostic - Investigates the prediction problem settings - use before training a model — diagnosePlp • PatientLevelPrediction - +
      - +

      diagnostic - Investigates the prediction problem settings - use before training a model

      - Source: R/DiagnosePlp.R + Source: R/DiagnosePlp.R
      -

      This function runs a set of prediction diagnoses to help pick a suitable T, O, TAR and determine +

      This function runs a set of prediction diagnoses to help pick a suitable T, O, TAR and determine whether the prediction problem is worth executing.

      @@ -152,47 +152,45 @@

      diagnostic - Investigates the prediction problem settings - use before train

      Arguments

      - - -
      plpData
      +
      plpData

      An object of type plpData - the patient level prediction -data extracted from the CDM. Can also include an initial population as +data extracted from the CDM. Can also include an initial population as plpData$popualtion.

      -
      outcomeId
      +
      outcomeId

      (integer) The ID of the outcome.

      -
      analysisId
      +
      analysisId

      (integer) Identifier for the analysis. It is used to create, e.g., the result folder. Default is a timestamp.

      -
      populationSettings
      +
      populationSettings

      An object of type populationSettings created using createStudyPopulationSettings that -specifies how the data class labels are defined and addition any exclusions to apply to the +specifies how the data class labels are defined and addition any exclusions to apply to the plpData cohort

      -
      splitSettings
      +
      splitSettings

      An object of type splitSettings that specifies how to split the data into train/validation/test. The default settings can be created using createDefaultSplitSetting.

      -
      sampleSettings
      +
      sampleSettings

      An object of type sampleSettings that specifies any under/over sampling to be done. The default is none.

      -
      saveDirectory
      +
      saveDirectory

      The path to the directory where the results will be saved (if NULL uses working directory)

      -
      featureEngineeringSettings
      +
      featureEngineeringSettings

      An object of featureEngineeringSettings specifying any feature engineering to be learned (using the train data)

      -
      modelSettings
      +
      modelSettings

      An object of class modelSettings created using one of the function:

      • setLassoLogisticRegression() A lasso logistic regression model

      • setGradientBoostingMachine() A gradient boosting machine

      • setAdaBoost() An ada boost model

        • @@ -202,20 +200,22 @@

        Arguments

      -
      logSettings
      -

      An object of logSettings created using createLogSettings +

      logSettings
      +

      An object of logSettings created using createLogSettings specifying how the logging is done

      -
      preprocessSettings
      -

      An object of preprocessSettings. This setting specifies the minimum fraction of -target population who must have a covariate for it to be included in the model training +

      preprocessSettings
      +

      An object of preprocessSettings. This setting specifies the minimum fraction of +target population who must have a covariate for it to be included in the model training and whether to normalise the covariates before training

      Value

      -

      An object containing the model or location where the model is save, the data selection settings, the preprocessing + + +

      An object containing the model or location where the model is save, the data selection settings, the preprocessing and training settings as well as various performance measures obtained by the model.

      distribution

      list for each O of a data.frame containing: i) Time to observation end distribution, ii) Time from observation start distribution, iii) Time to event distribution and iv) Time from last prior event to index distribution (only for patients in T who have O before index)

      @@ -230,15 +230,15 @@

      Value

      Details

      Users can define set of Ts, Os, databases and population settings. A list of data.frames containing details such as -follow-up time distribution, time-to-event information, characteriszation details, time from last prior event, +follow-up time distribution, time-to-event information, characteriszation details, time from last prior event, observation time distribution.

      Examples

      - 
      if (FALSE) { # \dontrun{
+    
      if (FALSE) {
 #******** EXAMPLE 1 ********* 
-} # } 
+}
      @@ -253,15 +253,15 @@

      Examples

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/diagnosticOddsRatio.html b/reference/diagnosticOddsRatio.html index c3aecafcd..806f33f02 100644 --- a/reference/diagnosticOddsRatio.html +++ b/reference/diagnosticOddsRatio.html @@ -1,9 +1,9 @@ -Calculate the diagnostic odds ratio — diagnosticOddsRatio • PatientLevelPredictionCalculate the diagnostic odds ratio — diagnosticOddsRatio • PatientLevelPrediction - +
      - +

      Calculate the diagnostic odds ratio

      - Source: R/ThresholdSummary.R + Source: R/ThresholdSummary.R
      @@ -137,27 +137,27 @@

      Calculate the diagnostic odds ratio

      Arguments

      - - -
      TP
      +
      TP

      Number of true positives

      -
      TN
      +
      TN

      Number of true negatives

      -
      FN
      +
      FN

      Number of false negatives

      -
      FP
      +
      FP

      Number of false positives

      Value

      -

      diagnosticOddsRatio value

      + + +

      diagnosticOddsRatio value

      Details

      @@ -176,15 +176,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/evaluatePlp.html b/reference/evaluatePlp.html index a507acdd3..7ad688156 100644 --- a/reference/evaluatePlp.html +++ b/reference/evaluatePlp.html @@ -1,9 +1,9 @@ -evaluatePlp — evaluatePlp • PatientLevelPredictionevaluatePlp — evaluatePlp • PatientLevelPrediction - +
      - +

      evaluatePlp

      - Source: R/EvaluatePlp.R + Source: R/EvaluatePlp.R
      @@ -137,20 +137,20 @@

      evaluatePlp

      Arguments

      - - -
      prediction
      +
      prediction

      The patient level prediction model's prediction

      -
      typeColumn
      -

      The column name in the prediction object that is used to +

      typeColumn
      +

      The column name in the prediction object that is used to stratify the evaluation

      Value

      -

      A list containing the performance values

      + + +

      A list containing the performance values

      Details

      @@ -169,15 +169,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/externalValidateDbPlp.html b/reference/externalValidateDbPlp.html index b59f23563..79f46b721 100644 --- a/reference/externalValidateDbPlp.html +++ b/reference/externalValidateDbPlp.html @@ -1,9 +1,9 @@ -externalValidateDbPlp - Validate a model on new databases — externalValidateDbPlp • PatientLevelPredictionexternalValidateDbPlp - Validate a model on new databases — externalValidateDbPlp • PatientLevelPrediction - +
      - +

      externalValidateDbPlp - Validate a model on new databases

      - Source: R/ExternalValidatePlp.R + Source: R/ExternalValidatePlp.R
      @@ -144,36 +144,36 @@

      externalValidateDbPlp - Validate a model on new databases

      Arguments

      - - -
      plpModel
      +
      plpModel

      The model object returned by runPlp() containing the trained model

      -
      validationDatabaseDetails
      +
      validationDatabaseDetails

      A list of objects of class databaseDetails created using createDatabaseDetails

      -
      validationRestrictPlpDataSettings
      +
      validationRestrictPlpDataSettings

      A list of population restriction settings created by createRestrictPlpDataSettings()

      -
      settings
      +
      settings

      A settings object of class validationSettings created using createValidationSettings

      -
      logSettings
      +
      logSettings

      An object of logSettings created using createLogSettings specifying how the logging is done

      -
      outputFolder
      +
      outputFolder

      The directory to save the validation results to (subfolders are created per database in validationDatabaseDetails)

      Value

      -

      A list containing the performance for each validation_schema

      + + +

      A list containing the performance for each validation_schema

      Details

      @@ -193,15 +193,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/extractDatabaseToCsv.html b/reference/extractDatabaseToCsv.html index f82920eda..8e90243ea 100644 --- a/reference/extractDatabaseToCsv.html +++ b/reference/extractDatabaseToCsv.html @@ -1,9 +1,9 @@ -Exports all the results from a database into csv files — extractDatabaseToCsv • PatientLevelPredictionExports all the results from a database into csv files — extractDatabaseToCsv • PatientLevelPrediction - +
      - +

      Exports all the results from a database into csv files

      - Source: R/SaveLoadPlp.R + Source: R/SaveLoadPlp.R
      @@ -145,33 +145,31 @@

      Exports all the results from a database into csv files

      Arguments

      - - -
      conn
      +
      conn

      The connection to the database with the results

      -
      connectionDetails
      +
      connectionDetails

      The connectionDetails for the result database

      -
      databaseSchemaSettings
      +
      databaseSchemaSettings

      The result database schema settings

      -
      csvFolder
      +
      csvFolder

      Location to save the csv files

      -
      minCellCount
      +
      minCellCount

      The min value to show in cells that are sensitive (values less than this value will be replaced with -1)

      -
      sensitiveColumns
      +
      sensitiveColumns

      A named list (name of table columns belong to) with a list of columns to apply the minCellCount to.

      -
      fileAppend
      +
      fileAppend

      If set to a string this will be appended to the start of the csv file names

      @@ -192,15 +190,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/f1Score.html b/reference/f1Score.html index 1660268f1..0a2fb3974 100644 --- a/reference/f1Score.html +++ b/reference/f1Score.html @@ -1,9 +1,9 @@ -Calculate the f1Score — f1Score • PatientLevelPredictionCalculate the f1Score — f1Score • PatientLevelPrediction - +
      - +

      Calculate the f1Score

      - Source: R/ThresholdSummary.R + Source: R/ThresholdSummary.R
      @@ -137,27 +137,27 @@

      Calculate the f1Score

      Arguments

      - - -
      TP
      +
      TP

      Number of true positives

      -
      TN
      +
      TN

      Number of true negatives

      -
      FN
      +
      FN

      Number of false negatives

      -
      FP
      +
      FP

      Number of false positives

      Value

      -

      f1Score value

      + + +

      f1Score value

      Details

      @@ -176,15 +176,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/falseDiscoveryRate.html b/reference/falseDiscoveryRate.html index d5333a75d..73f4dc46b 100644 --- a/reference/falseDiscoveryRate.html +++ b/reference/falseDiscoveryRate.html @@ -1,9 +1,9 @@ -Calculate the falseDiscoveryRate — falseDiscoveryRate • PatientLevelPredictionCalculate the falseDiscoveryRate — falseDiscoveryRate • PatientLevelPrediction - +
      - +

      Calculate the falseDiscoveryRate

      - Source: R/ThresholdSummary.R + Source: R/ThresholdSummary.R
      @@ -137,27 +137,27 @@

      Calculate the falseDiscoveryRate

      Arguments

      - - -
      TP
      +
      TP

      Number of true positives

      -
      TN
      +
      TN

      Number of true negatives

      -
      FN
      +
      FN

      Number of false negatives

      -
      FP
      +
      FP

      Number of false positives

      Value

      -

      falseDiscoveryRate value

      + + +

      falseDiscoveryRate value

      Details

      @@ -176,15 +176,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/falseNegativeRate.html b/reference/falseNegativeRate.html index bd82d1960..dc984b78f 100644 --- a/reference/falseNegativeRate.html +++ b/reference/falseNegativeRate.html @@ -1,9 +1,9 @@ -Calculate the falseNegativeRate — falseNegativeRate • PatientLevelPredictionCalculate the falseNegativeRate — falseNegativeRate • PatientLevelPrediction - +
      - +

      Calculate the falseNegativeRate

      - Source: R/ThresholdSummary.R + Source: R/ThresholdSummary.R
      @@ -137,27 +137,27 @@

      Calculate the falseNegativeRate

      Arguments

      - - -
      TP
      +
      TP

      Number of true positives

      -
      TN
      +
      TN

      Number of true negatives

      -
      FN
      +
      FN

      Number of false negatives

      -
      FP
      +
      FP

      Number of false positives

      Value

      -

      falseNegativeRate value

      + + +

      falseNegativeRate value

      Details

      @@ -176,15 +176,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/falseOmissionRate.html b/reference/falseOmissionRate.html index c889090b4..7cf484107 100644 --- a/reference/falseOmissionRate.html +++ b/reference/falseOmissionRate.html @@ -1,9 +1,9 @@ -Calculate the falseOmissionRate — falseOmissionRate • PatientLevelPredictionCalculate the falseOmissionRate — falseOmissionRate • PatientLevelPrediction - +
      - +

      Calculate the falseOmissionRate

      - Source: R/ThresholdSummary.R + Source: R/ThresholdSummary.R
      @@ -137,27 +137,27 @@

      Calculate the falseOmissionRate

      Arguments

      - - -
      TP
      +
      TP

      Number of true positives

      -
      TN
      +
      TN

      Number of true negatives

      -
      FN
      +
      FN

      Number of false negatives

      -
      FP
      +
      FP

      Number of false positives

      Value

      -

      falseOmissionRate value

      + + +

      falseOmissionRate value

      Details

      @@ -176,15 +176,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/falsePositiveRate.html b/reference/falsePositiveRate.html index d6a9151ab..0f378c3c8 100644 --- a/reference/falsePositiveRate.html +++ b/reference/falsePositiveRate.html @@ -1,9 +1,9 @@ -Calculate the falsePositiveRate — falsePositiveRate • PatientLevelPredictionCalculate the falsePositiveRate — falsePositiveRate • PatientLevelPrediction - +
      - +

      Calculate the falsePositiveRate

      - Source: R/ThresholdSummary.R + Source: R/ThresholdSummary.R
      @@ -137,27 +137,27 @@

      Calculate the falsePositiveRate

      Arguments

      - - -
      TP
      +
      TP

      Number of true positives

      -
      TN
      +
      TN

      Number of true negatives

      -
      FN
      +
      FN

      Number of false negatives

      -
      FP
      +
      FP

      Number of false positives

      Value

      -

      falsePositiveRate value

      + + +

      falsePositiveRate value

      Details

      @@ -176,15 +176,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/fitPlp.html b/reference/fitPlp.html index cc93e7644..6a394ecec 100644 --- a/reference/fitPlp.html +++ b/reference/fitPlp.html @@ -1,9 +1,9 @@ -fitPlp — fitPlp • PatientLevelPredictionfitPlp — fitPlp • PatientLevelPrediction - +
      - +

      fitPlp

      - Source: R/Fit.R + Source: R/Fit.R
      @@ -137,14 +137,12 @@

      fitPlp

      Arguments

      - - -
      trainData
      +
      trainData

      An object of type TrainData created using splitData data extracted from the CDM.

      -
      modelSettings
      +
      modelSettings

      An object of class modelSettings created using one of the function:

      • setLassoLogisticRegression() A lasso logistic regression model

      • setGradientBoostingMachine() A gradient boosting machine

      • setRandomForest() A random forest model

        • @@ -152,21 +150,23 @@

        Arguments

      - +
      search

      The search strategy for the hyper-parameter selection (currently not used)

      -
      analysisId
      +
      analysisId

      The id of the analysis

      -
      analysisPath
      +
      analysisPath

      The path of the analysis

      Value

      -

      An object of class plpModel containing:

      + + +

      An object of class plpModel containing:

      model

      The trained prediction model

      @@ -204,15 +204,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/getCalibrationSummary.html b/reference/getCalibrationSummary.html index be3edfe3a..f32e764d0 100644 --- a/reference/getCalibrationSummary.html +++ b/reference/getCalibrationSummary.html @@ -1,9 +1,9 @@ -Get a sparse summary of the calibration — getCalibrationSummary • PatientLevelPredictionGet a sparse summary of the calibration — getCalibrationSummary • PatientLevelPrediction - +
      - +

      Get a sparse summary of the calibration

      - Source: R/CalibrationSummary.R + Source: R/CalibrationSummary.R
      @@ -143,33 +143,33 @@

      Get a sparse summary of the calibration

      Arguments

      - - -
      prediction
      +
      prediction

      A prediction object as generated using the predict functions.

      -
      predictionType
      +
      predictionType

      The type of prediction (binary or survival)

      -
      typeColumn
      +
      typeColumn

      A column that is used to stratify the results

      -
      numberOfStrata
      +
      numberOfStrata

      The number of strata in the plot.

      -
      truncateFraction
      +
      truncateFraction

      This fraction of probability values will be ignored when plotting, to avoid the x-axis scale being dominated by a few outliers.

      Value

      -

      A dataframe with the calibration summary

      + + +

      A dataframe with the calibration summary

      Details

      @@ -189,15 +189,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/getCohortCovariateData.html b/reference/getCohortCovariateData.html index 6b57a09fd..b5dcda0a1 100644 --- a/reference/getCohortCovariateData.html +++ b/reference/getCohortCovariateData.html @@ -1,9 +1,9 @@ -Extracts covariates based on cohorts — getCohortCovariateData • PatientLevelPredictionExtracts covariates based on cohorts — getCohortCovariateData • PatientLevelPrediction - +
      - +

      Extracts covariates based on cohorts

      - Source: R/AdditionalCovariates.R + Source: R/AdditionalCovariates.R
      @@ -148,51 +148,51 @@

      Extracts covariates based on cohorts

      Arguments

      - - -
      connection
      +
      connection

      The database connection

      -
      oracleTempSchema
      +
      oracleTempSchema

      The temp schema if using oracle

      -
      cdmDatabaseSchema
      +
      cdmDatabaseSchema

      The schema of the OMOP CDM data

      -
      cdmVersion
      +
      cdmVersion

      version of the OMOP CDM data

      -
      cohortTable
      +
      cohortTable

      the table name that contains the target population cohort

      -
      rowIdField
      +
      rowIdField

      string representing the unique identifier in the target population cohort

      -
      aggregated
      +
      aggregated

      whether the covariate should be aggregated

      -
      cohortIds
      +
      cohortIds

      cohort id for the target cohort

      -
      covariateSettings
      +
      covariateSettings

      settings for the covariate cohorts and time periods

      -
      ...
      +
      ...

      additional arguments from FeatureExtraction

      Value

      -

      The models will now be in the package

      + + +

      The models will now be in the package

      Details

      @@ -212,15 +212,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/getDemographicSummary.html b/reference/getDemographicSummary.html index 6b3b80ae9..a4ffe6cdc 100644 --- a/reference/getDemographicSummary.html +++ b/reference/getDemographicSummary.html @@ -1,9 +1,9 @@ -Get a calibration per age/gender groups — getDemographicSummary • PatientLevelPredictionGet a calibration per age/gender groups — getDemographicSummary • PatientLevelPrediction - +
      - +

      Get a calibration per age/gender groups

      - Source: R/DemographicSummary.R + Source: R/DemographicSummary.R
      @@ -137,23 +137,23 @@

      Get a calibration per age/gender groups

      Arguments

      - - -
      prediction
      +
      prediction

      A prediction object

      -
      predictionType
      +
      predictionType

      The type of prediction (binary or survival)

      -
      typeColumn
      +
      typeColumn

      A column that is used to stratify the results

      Value

      -

      A dataframe with the calibration summary

      + + +

      A dataframe with the calibration summary

      Details

      @@ -172,15 +172,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/getPlpData.html b/reference/getPlpData.html index 77d717bca..88c691ae6 100644 --- a/reference/getPlpData.html +++ b/reference/getPlpData.html @@ -1,10 +1,10 @@ -Get the patient level prediction data from the server — getPlpData • PatientLevelPredictionGet the patient level prediction data from the server — getPlpData • PatientLevelPrediction - +
      - +

      Get the patient level prediction data from the server

      - Source: R/ExtractData.R + Source: R/ExtractData.R
      @@ -139,25 +139,25 @@

      Get the patient level prediction data from the server

      Arguments

      - - -
      databaseDetails
      +
      databaseDetails

      The cdm database details created using createDatabaseDetails()

      -
      covariateSettings
      +
      covariateSettings

      An object of type covariateSettings as created using the createCovariateSettings function in the FeatureExtraction package.

      -
      restrictPlpDataSettings
      +
      restrictPlpDataSettings

      Extra settings to apply to the target population while extracting data. Created using createRestrictPlpDataSettings().

      Value

      -

      Returns an object of type plpData, containing information on the cohorts, their + + +

      Returns an object of type plpData, containing information on the cohorts, their outcomes, and baseline covariates. Information about multiple outcomes can be captured at once for efficiency reasons. This object is a list with the following components:

      outcomes

      A data frame listing the outcomes per person, including the time to event, and @@ -177,7 +177,7 @@

      Value

      metaData

      A list of objects with information on how the cohortMethodData object was constructed.

      - +

      The generic () and summary() functions have been implemented for this object.

      @@ -185,7 +185,7 @@

      Details

      Based on the arguments, the at risk cohort data is retrieved, as well as outcomes occurring in these subjects. The at risk cohort is identified through user-defined cohorts in a cohort table either inside the CDM instance or in a separate schema. -Similarly, outcomes are identified +Similarly, outcomes are identified through user-defined cohorts in a cohort table either inside the CDM instance or in a separate schema. Covariates are automatically extracted from the appropriate tables within the CDM. If you wish to exclude concepts from covariates you will need to @@ -205,15 +205,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/getPredictionDistribution.html b/reference/getPredictionDistribution.html index d196ecd96..f6d9eac2f 100644 --- a/reference/getPredictionDistribution.html +++ b/reference/getPredictionDistribution.html @@ -1,9 +1,9 @@ -Calculates the prediction distribution — getPredictionDistribution • PatientLevelPredictionCalculates the prediction distribution — getPredictionDistribution • PatientLevelPrediction - +
      - +

      Calculates the prediction distribution

      - Source: R/PredictionDistribution.R + Source: R/PredictionDistribution.R
      @@ -141,23 +141,23 @@

      Calculates the prediction distribution

      Arguments

      - - -
      prediction
      +
      prediction

      A prediction object

      -
      predictionType
      +
      predictionType

      The type of prediction (binary or survival)

      -
      typeColumn
      +
      typeColumn

      A column that is used to stratify the results

      Value

      -

      The 0.00, 0.1, 0.25, 0.5, 0.75, 0.9, 1.00 quantile pf the prediction, + + +

      The 0.00, 0.1, 0.25, 0.5, 0.75, 0.9, 1.00 quantile pf the prediction, the mean and standard deviation per class

      @@ -177,15 +177,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/getPredictionDistribution_binary.html b/reference/getPredictionDistribution_binary.html index 525c868f4..25312a70d 100644 --- a/reference/getPredictionDistribution_binary.html +++ b/reference/getPredictionDistribution_binary.html @@ -1,9 +1,9 @@ -Calculates the prediction distribution — getPredictionDistribution_binary • PatientLevelPredictionCalculates the prediction distribution — getPredictionDistribution_binary • PatientLevelPrediction - +
      - +

      Calculates the prediction distribution

      - Source: R/PredictionDistribution.R + Source: R/PredictionDistribution.R
      @@ -137,23 +137,23 @@

      Calculates the prediction distribution

      Arguments

      - - -
      prediction
      +
      prediction

      A prediction object

      -
      evalColumn
      +
      evalColumn

      A column that is used to stratify the results

      -
      ...
      +
      ...

      Other inputs

      Value

      -

      The 0.00, 0.1, 0.25, 0.5, 0.75, 0.9, 1.00 quantile pf the prediction, + + +

      The 0.00, 0.1, 0.25, 0.5, 0.75, 0.9, 1.00 quantile pf the prediction, the mean and standard deviation per class

      @@ -173,15 +173,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/getThresholdSummary.html b/reference/getThresholdSummary.html index 78009d7b8..743440c53 100644 --- a/reference/getThresholdSummary.html +++ b/reference/getThresholdSummary.html @@ -1,9 +1,9 @@ -Calculate all measures for sparse ROC — getThresholdSummary • PatientLevelPredictionCalculate all measures for sparse ROC — getThresholdSummary • PatientLevelPrediction - +
      - +

      Calculate all measures for sparse ROC

      - Source: R/ThresholdSummary.R + Source: R/ThresholdSummary.R
      @@ -137,23 +137,23 @@

      Calculate all measures for sparse ROC

      Arguments

      - - -
      prediction
      +
      prediction

      A prediction object

      -
      predictionType
      +
      predictionType

      The type of prediction (binary or survival)

      -
      typeColumn
      +
      typeColumn

      A column that is used to stratify the results

      Value

      -

      A data.frame with all the measures

      + + +

      A data.frame with all the measures

      Details

      @@ -173,15 +173,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/getThresholdSummary_binary.html b/reference/getThresholdSummary_binary.html index 9a22ba5b7..dd31368b0 100644 --- a/reference/getThresholdSummary_binary.html +++ b/reference/getThresholdSummary_binary.html @@ -1,9 +1,9 @@ -Calculate all measures for sparse ROC when prediction is bianry classification — getThresholdSummary_binary • PatientLevelPredictionCalculate all measures for sparse ROC when prediction is bianry classification — getThresholdSummary_binary • PatientLevelPrediction - +
      - +

      Calculate all measures for sparse ROC when prediction is bianry classification

      - Source: R/ThresholdSummary.R + Source: R/ThresholdSummary.R
      @@ -137,23 +137,23 @@

      Calculate all measures for sparse ROC when prediction is bianry classificati

      Arguments

      - - -
      prediction
      +
      prediction

      A prediction object

      -
      evalColumn
      +
      evalColumn

      A column that is used to stratify the results

      -
      ...
      +
      ...

      Other inputs

      Value

      -

      A data.frame with all the measures

      + + +

      A data.frame with all the measures

      Details

      @@ -173,15 +173,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/ici.html b/reference/ici.html index 6ee6f4846..aefd353ec 100644 --- a/reference/ici.html +++ b/reference/ici.html @@ -1,10 +1,12 @@ -Calculate the Integrated Calibration Information from Austin and Steyerberg https://onlinelibrary.wiley.com/doi/full/10.1002/sim.8281 — ici • PatientLevelPredictionCalculate the Integrated Calibration Information from Austin and Steyerberg +https://onlinelibrary.wiley.com/doi/full/10.1002/sim.8281 — ici • PatientLevelPrediction - +
      - +
      -

      Calculate the Integrated Calibration Information from Austin and Steyerberg https://onlinelibrary.wiley.com/doi/full/10.1002/sim.8281

      - Source: R/EvaluationSummary.R +

      Calculate the Integrated Calibration Information from Austin and Steyerberg +https://onlinelibrary.wiley.com/doi/full/10.1002/sim.8281

      + Source: R/EvaluationSummary.R
      @@ -139,15 +142,15 @@

      Calculate the Integrated Calibration Information from Austin and Steyerberg

      Arguments

      - - -
      prediction
      +
      prediction

      the prediction object found in the plpResult object

      Value

      -

      Integrated Calibration Information

      + + +

      Integrated Calibration Information

      Details

      @@ -166,15 +169,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/index.html b/reference/index.html index 131bbe705..652594c5b 100644 --- a/reference/index.html +++ b/reference/index.html @@ -1,9 +1,9 @@ -Package index • PatientLevelPredictionFunction reference • PatientLevelPrediction - +
      - +
      @@ -164,11 +164,13 @@

      Settings for designing a pre

      createDefaultSplitSetting()

      Create the settings for defining how the plpData are split into test/validation/train sets using default splitting functions (either random stratified by outcome, time or subject splitting)

      Create the settings for defining how the plpData are split into test/validation/train sets using +default splitting functions (either random stratified by outcome, time or subject splitting)

      createSampleSettings()

      Create the settings for defining how the trainData from splitData are sampled using default sample functions.

      Create the settings for defining how the trainData from splitData are sampled using +default sample functions.

      createFeatureEngineeringSettings()

      A function that wraps around FeatureExtraction::tidyCovariateData to normalise the data and remove rare or redundant features

      A function that wraps around FeatureExtraction::tidyCovariateData to normalise the data +and remove rare or redundant features

      fitPlp()

      plotSmoothCalibration()

      Plot the smooth calibration as detailed in Calster et al. "A calibration heirarchy for risk models was defined: from utopia to empirical data" (2016)

      Plot the smooth calibration as detailed in Calster et al. "A calibration heirarchy for risk models +was defined: from utopia to empirical data" (2016)

      plotSparseCalibration()

      plotPreferencePDF()

      Plot the preference score probability density function, showing prediction overlap between true and false cases #'

      Plot the preference score probability density function, showing prediction overlap between true and false cases +#'

      plotPredictionDistribution()

      ici()

      Calculate the Integrated Calibration Information from Austin and Steyerberg https://onlinelibrary.wiley.com/doi/full/10.1002/sim.8281

      Calculate the Integrated Calibration Information from Austin and Steyerberg +https://onlinelibrary.wiley.com/doi/full/10.1002/sim.8281

      modelBasedConcordance()

      Calculate the model-based concordance, which is a calculation of the expected discrimination performance of a model under the assumption the model predicts the "TRUE" outcome as detailed in van Klaveren et al. https://pubmed.ncbi.nlm.nih.gov/27251001/

      Calculate the model-based concordance, which is a calculation of the expected discrimination performance of a model under the assumption the model predicts the "TRUE" outcome +as detailed in van Klaveren et al. https://pubmed.ncbi.nlm.nih.gov/27251001/

      negativeLikelihoodRatio()

      analysisIdThe unique identifier + + +

      A data frame with the following columns:

      analysisIdThe unique identifier for a set of analysis choices.
      targetIdThe ID of the target cohort populations.
      outcomeIdThe ID of the outcomeId.
      dataLocationThe location where the plpData was saved
      the settings idsThe ids for all other settings used for model development.

      Details

      @@ -199,15 +199,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/runPlp.html b/reference/runPlp.html index 2694d64da..2eb237596 100644 --- a/reference/runPlp.html +++ b/reference/runPlp.html @@ -1,5 +1,5 @@ -runPlp - Develop and internally evaluate a model using specified settings — runPlp • PatientLevelPredictionrunPlp - Develop and internally evaluate a model using specified settings — runPlp • PatientLevelPrediction - +
      - +

      runPlp - Develop and internally evaluate a model using specified settings

      - Source: R/RunPlp.R + Source: R/RunPlp.R
      -

      This provides a general framework for training patient level prediction models. The user can select +

      This provides a general framework for training patient level prediction models. The user can select various default feature selection methods or incorporate their own, The user can also select from a range of default classifiers or incorporate their own. There are three types of evaluations for the model patient (randomly splits people into train/validation sets) or year (randomly splits data into train/validation sets @@ -163,53 +163,51 @@

      runPlp - Develop and internally evaluate a model using specified settings

      Arguments

      - - -
      plpData
      +
      plpData

      An object of type plpData - the patient level prediction -data extracted from the CDM. Can also include an initial population as +data extracted from the CDM. Can also include an initial population as plpData$popualtion.

      -
      outcomeId
      +
      outcomeId

      (integer) The ID of the outcome.

      -
      analysisId
      +
      analysisId

      (integer) Identifier for the analysis. It is used to create, e.g., the result folder. Default is a timestamp.

      -
      analysisName
      +
      analysisName

      (character) Name for the analysis

      -
      populationSettings
      +
      populationSettings

      An object of type populationSettings created using createStudyPopulationSettings that -specifies how the data class labels are defined and addition any exclusions to apply to the +specifies how the data class labels are defined and addition any exclusions to apply to the plpData cohort

      -
      splitSettings
      +
      splitSettings

      An object of type splitSettings that specifies how to split the data into train/validation/test. The default settings can be created using createDefaultSplitSetting.

      -
      sampleSettings
      +
      sampleSettings

      An object of type sampleSettings that specifies any under/over sampling to be done. The default is none.

      -
      featureEngineeringSettings
      +
      featureEngineeringSettings

      An object of featureEngineeringSettings specifying any feature engineering to be learned (using the train data)

      -
      preprocessSettings
      -

      An object of preprocessSettings. This setting specifies the minimum fraction of -target population who must have a covariate for it to be included in the model training +

      preprocessSettings
      +

      An object of preprocessSettings. This setting specifies the minimum fraction of +target population who must have a covariate for it to be included in the model training and whether to normalise the covariates before training

      -
      modelSettings
      +
      modelSettings

      An object of class modelSettings created using one of the function:

      • setLassoLogisticRegression() A lasso logistic regression model

      • setGradientBoostingMachine() A gradient boosting machine

      • setAdaBoost() An ada boost model

        • @@ -219,23 +217,26 @@

        Arguments

      -
      logSettings
      -

      An object of logSettings created using createLogSettings +

      logSettings
      +

      An object of logSettings created using createLogSettings specifying how the logging is done

      -
      executeSettings
      +
      executeSettings

      An object of executeSettings specifying which parts of the analysis to run

      -
      saveDirectory
      +
      saveDirectory

      The path to the directory where the results will be saved (if NULL uses working directory)

      Value

      -

      An object containing the following:

      -

      • model The developed model of class plpModel

        • + + +

        An object containing the following:

        +

        +

        • model The developed model of class plpModel

        • executionSummary A list containing the hardward details, R package details and execution time

        • performanceEvaluation Various internal performance metrics in sparse format

        • prediction The plpData cohort table with the predicted risks added as a column (named value)

          • @@ -250,7 +251,7 @@

          Details

          Examples

          - +
      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/savePlpAnalysesJson.html b/reference/savePlpAnalysesJson.html index 16a9b006d..675b05e57 100644 --- a/reference/savePlpAnalysesJson.html +++ b/reference/savePlpAnalysesJson.html @@ -1,9 +1,9 @@ -Save the modelDesignList to a json file — savePlpAnalysesJson • PatientLevelPredictionSave the modelDesignList to a json file — savePlpAnalysesJson • PatientLevelPrediction - +
      - +

      Save the modelDesignList to a json file

      - Source: R/RunMultiplePlp.R + Source: R/RunMultiplePlp.R
      @@ -143,17 +143,15 @@

      Save the modelDesignList to a json file

      Arguments

      - - -
      modelDesignList
      +
      modelDesignList

      A list of modelDesigns created using createModelDesign()

      -
      cohortDefinitions
      +
      cohortDefinitions

      A list of the cohortDefinitions (generally extracted from ATLAS)

      -
      saveDirectory
      +
      saveDirectory

      The directory to save the modelDesignList settings

      @@ -164,7 +162,7 @@

      Details

      Examples

      - 
      if (FALSE) { # \dontrun{
+    
      if (FALSE) {
 savePlpAnalysesJson(
 modelDesignList = list(
 createModelDesign(targetId = 1, outcomeId = 2, modelSettings = setLassoLogisticRegression()), 
@@ -172,7 +170,7 @@ 
      Examples
      
 ),
 saveDirectory = 'C:/bestModels'
 )
-} # }
+}
      @@ -188,15 +186,15 @@

      Examples

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/savePlpData.html b/reference/savePlpData.html index c64913a62..54a2af0ae 100644 --- a/reference/savePlpData.html +++ b/reference/savePlpData.html @@ -1,9 +1,9 @@ -Save the cohort data to folder — savePlpData • PatientLevelPredictionSave the cohort data to folder — savePlpData • PatientLevelPrediction - +
      - +

      Save the cohort data to folder

      - Source: R/SaveLoadPlp.R + Source: R/SaveLoadPlp.R
      @@ -137,23 +137,21 @@

      Save the cohort data to folder

      Arguments

      - - -
      plpData
      +
      plpData

      An object of type plpData as generated using getPlpData.

      -
      file
      +
      file

      The name of the folder where the data will be written. The folder should not yet exist.

      -
      envir
      +
      envir

      The environment for to evaluate variables when saving

      -
      overwrite
      +
      overwrite

      Whether to force overwrite an existing file

      @@ -181,15 +179,15 @@

      Examples

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/savePlpModel.html b/reference/savePlpModel.html index 3abd856c4..786674942 100644 --- a/reference/savePlpModel.html +++ b/reference/savePlpModel.html @@ -1,9 +1,9 @@ -Saves the plp model — savePlpModel • PatientLevelPredictionSaves the plp model — savePlpModel • PatientLevelPrediction - +
      - +

      Saves the plp model

      - Source: R/SaveLoadPlp.R + Source: R/SaveLoadPlp.R
      @@ -137,13 +137,11 @@

      Saves the plp model

      Arguments

      - - -
      plpModel
      +
      plpModel

      A trained classifier returned by running runPlp()$model

      -
      dirPath
      +
      dirPath

      A location to save the model to

      @@ -164,15 +162,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/savePlpResult.html b/reference/savePlpResult.html index cd77dcc33..52064bba2 100644 --- a/reference/savePlpResult.html +++ b/reference/savePlpResult.html @@ -1,9 +1,9 @@ -Saves the result from runPlp into the location directory — savePlpResult • PatientLevelPredictionSaves the result from runPlp into the location directory — savePlpResult • PatientLevelPrediction - +
      - +

      Saves the result from runPlp into the location directory

      - Source: R/SaveLoadPlp.R + Source: R/SaveLoadPlp.R
      @@ -137,13 +137,11 @@

      Saves the result from runPlp into the location directory

      Arguments

      - - -
      result
      +
      result

      The result of running runPlp()

      -
      dirPath
      +
      dirPath

      The directory to save the csv

      @@ -164,15 +162,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/savePlpShareable.html b/reference/savePlpShareable.html index 90291a76a..828518125 100644 --- a/reference/savePlpShareable.html +++ b/reference/savePlpShareable.html @@ -1,9 +1,9 @@ -Save the plp result as json files and csv files for transparent sharing — savePlpShareable • PatientLevelPredictionSave the plp result as json files and csv files for transparent sharing — savePlpShareable • PatientLevelPrediction - +
      - +

      Save the plp result as json files and csv files for transparent sharing

      - Source: R/SaveLoadPlp.R + Source: R/SaveLoadPlp.R
      @@ -137,17 +137,15 @@

      Save the plp result as json files and csv files for transparent sharing

      Arguments

      - - -
      result
      +
      result

      An object of class runPlp with development or validation results

      -
      saveDirectory
      +
      saveDirectory

      The directory the save the results as csv files

      -
      minCellCount
      +
      minCellCount

      Minimum cell count for the covariateSummary and certain evaluation results

      @@ -168,15 +166,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/savePrediction.html b/reference/savePrediction.html index eb85bdb41..6ff5a6ff3 100644 --- a/reference/savePrediction.html +++ b/reference/savePrediction.html @@ -1,9 +1,9 @@ -Saves the prediction dataframe to RDS — savePrediction • PatientLevelPredictionSaves the prediction dataframe to RDS — savePrediction • PatientLevelPrediction - +
      - +

      Saves the prediction dataframe to RDS

      - Source: R/SaveLoadPlp.R + Source: R/SaveLoadPlp.R
      @@ -137,17 +137,15 @@

      Saves the prediction dataframe to RDS

      Arguments

      - - -
      prediction
      +
      prediction

      The prediciton data.frame

      -
      dirPath
      +
      dirPath

      The directory to save the prediction RDS

      -
      fileName
      +
      fileName

      The name of the RDS file that will be saved in dirPath

      @@ -168,15 +166,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/sensitivity.html b/reference/sensitivity.html index e836c7eea..512fa41d5 100644 --- a/reference/sensitivity.html +++ b/reference/sensitivity.html @@ -1,9 +1,9 @@ -Calculate the sensitivity — sensitivity • PatientLevelPredictionCalculate the sensitivity — sensitivity • PatientLevelPrediction - +
      - +

      Calculate the sensitivity

      - Source: R/ThresholdSummary.R + Source: R/ThresholdSummary.R
      @@ -137,27 +137,27 @@

      Calculate the sensitivity

      Arguments

      - - -
      TP
      +
      TP

      Number of true positives

      -
      TN
      +
      TN

      Number of true negatives

      -
      FN
      +
      FN

      Number of false negatives

      -
      FP
      +
      FP

      Number of false positives

      Value

      -

      sensitivity value

      + + +

      sensitivity value

      Details

      @@ -176,15 +176,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/setAdaBoost.html b/reference/setAdaBoost.html index 5d1bae24b..2ad4c4aea 100644 --- a/reference/setAdaBoost.html +++ b/reference/setAdaBoost.html @@ -1,9 +1,9 @@ -Create setting for AdaBoost with python DecisionTreeClassifier base estimator — setAdaBoost • PatientLevelPredictionCreate setting for AdaBoost with python DecisionTreeClassifier base estimator — setAdaBoost • PatientLevelPrediction - +
      - +

      Create setting for AdaBoost with python DecisionTreeClassifier base estimator

      - Source: R/SklearnClassifierSettings.R + Source: R/SklearnClassifierSettings.R
      @@ -142,33 +142,31 @@

      Create setting for AdaBoost with python DecisionTreeClassifier base estimato

      Arguments

      - - -
      nEstimators
      +
      nEstimators

      (list) The maximum number of estimators at which boosting is terminated. In case of perfect fit, the learning procedure is stopped early.

      -
      learningRate
      +
      learningRate

      (list) Weight applied to each classifier at each boosting iteration. A higher learning rate increases the contribution of each classifier. There is a trade-off between the learningRate and nEstimators parameters There is a trade-off between learningRate and nEstimators.

      -
      algorithm
      +
      algorithm

      (list) If ‘SAMME.R’ then use the SAMME.R real boosting algorithm. base_estimator must support calculation of class probabilities. If ‘SAMME’ then use the SAMME discrete boosting algorithm. The SAMME.R algorithm typically converges faster than SAMME, achieving a lower test error with fewer boosting iterations.

      -
      seed
      +
      seed

      A seed for the model

      Examples

      - 
      if (FALSE) { # \dontrun{
+    
      if (FALSE) {
 model.adaBoost <- setAdaBoost(nEstimators = list(10,50,200), learningRate = list(1, 0.5, 0.1),
                               algorithm = list('SAMME.R'), seed = sample(1000000,1)
                               )
-} # }
+}
      @@ -183,15 +181,15 @@

      Examples

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/setCoxModel.html b/reference/setCoxModel.html index 05ba4fd62..072cb424c 100644 --- a/reference/setCoxModel.html +++ b/reference/setCoxModel.html @@ -1,9 +1,9 @@ -Create setting for lasso Cox model — setCoxModel • PatientLevelPredictionCreate setting for lasso Cox model — setCoxModel • PatientLevelPrediction - +
      - +

      Create setting for lasso Cox model

      - Source: R/CyclopsSettings.R + Source: R/CyclopsSettings.R
      @@ -147,41 +147,39 @@

      Create setting for lasso Cox model

      Arguments

      - - -
      variance
      +
      variance

      Numeric: prior distribution starting variance

      -
      seed
      +
      seed

      An option to add a seed when training the model

      -
      includeCovariateIds
      +
      includeCovariateIds

      a set of covariate IDS to limit the analysis to

      -
      noShrinkage
      +
      noShrinkage

      a set of covariates whcih are to be forced to be included in the final model. default is the intercept

      -
      threads
      +
      threads

      An option to set number of threads when training model

      -
      upperLimit
      +
      upperLimit

      Numeric: Upper prior variance limit for grid-search

      -
      lowerLimit
      +
      lowerLimit

      Numeric: Lower prior variance limit for grid-search

      -
      tolerance
      +
      tolerance

      Numeric: maximum relative change in convergence criterion from successive iterations to achieve convergence

      -
      maxIterations
      +
      maxIterations

      Integer: maximum iterations of Cyclops to attempt before returning a failed-to-converge error

      @@ -203,15 +201,15 @@

      Examples

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/setDecisionTree.html b/reference/setDecisionTree.html index 7bad6a70f..1a6f0906f 100644 --- a/reference/setDecisionTree.html +++ b/reference/setDecisionTree.html @@ -1,9 +1,9 @@ -Create setting for the scikit-learn 1.0.1 DecisionTree with python — setDecisionTree • PatientLevelPredictionCreate setting for the scikit-learn 1.0.1 DecisionTree with python — setDecisionTree • PatientLevelPrediction - +
      - +

      Create setting for the scikit-learn 1.0.1 DecisionTree with python

      - Source: R/SklearnClassifierSettings.R + Source: R/SklearnClassifierSettings.R
      @@ -149,58 +149,56 @@

      Create setting for the scikit-learn 1.0.1 DecisionTree with python

      Arguments

      - - -
      criterion
      +
      criterion

      The function to measure the quality of a split. Supported criteria are “gini” for the Gini impurity and “entropy” for the information gain.

      -
      splitter
      +
      splitter

      The strategy used to choose the split at each node. Supported strategies are “best” to choose the best split and “random” to choose the best random split.

      -
      maxDepth
      +
      maxDepth

      (list) The maximum depth of the tree. If NULL, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples.

      -
      minSamplesSplit
      +
      minSamplesSplit

      The minimum number of samples required to split an internal node

      -
      minSamplesLeaf
      +
      minSamplesLeaf

      The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least minSamplesLeaf training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression.

      -
      minWeightFractionLeaf
      +
      minWeightFractionLeaf

      The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sampleWeight is not provided.

      -
      maxFeatures
      +
      maxFeatures

      (list) The number of features to consider when looking for the best split (int/'sqrt'/NULL)

      -
      maxLeafNodes
      +
      maxLeafNodes

      (list) Grow a tree with max_leaf_nodes in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes. (int/NULL)

      -
      minImpurityDecrease
      +
      minImpurityDecrease

      Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf.

      -
      classWeight
      +
      classWeight

      (list) Weights associated with classes 'balance' or NULL

      -
      seed
      +
      seed

      The random state seed

      Examples

      - 
      if (FALSE) { # \dontrun{
+    
      if (FALSE) {
 model.decisionTree <- setDecisionTree(maxDepth=10,minSamplesLeaf=10, seed=NULL )
-} # }
+}
      @@ -215,15 +213,15 @@

      Examples

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/setGradientBoostingMachine.html b/reference/setGradientBoostingMachine.html index eab53641c..125a8d43b 100644 --- a/reference/setGradientBoostingMachine.html +++ b/reference/setGradientBoostingMachine.html @@ -1,9 +1,9 @@ -Create setting for gradient boosting machine model using gbm_xgboost implementation — setGradientBoostingMachine • PatientLevelPredictionCreate setting for gradient boosting machine model using gbm_xgboost implementation — setGradientBoostingMachine • PatientLevelPrediction - +
      - +

      Create setting for gradient boosting machine model using gbm_xgboost implementation

      - Source: R/GradientBoostingMachine.R + Source: R/GradientBoostingMachine.R
      @@ -148,45 +148,43 @@

      Create setting for gradient boosting machine model using gbm_xgboost impleme

      Arguments

      - - -
      ntrees
      +
      ntrees

      The number of trees to build

      -
      nthread
      +
      nthread

      The number of computer threads to use (how many cores do you have?)

      -
      earlyStopRound
      +
      earlyStopRound

      If the performance does not increase over earlyStopRound number of trees then training stops (this prevents overfitting)

      -
      maxDepth
      +
      maxDepth

      Maximum depth of each tree - a large value will lead to slow model training

      -
      minChildWeight
      +
      minChildWeight

      Minimum sum of of instance weight in a child node - larger values are more conservative

      -
      learnRate
      +
      learnRate

      The boosting learn rate

      -
      scalePosWeight
      +
      scalePosWeight

      Controls weight of positive class in loss - useful for imbalanced classes

      -
      lambda
      +
      lambda

      L2 regularization on weights - larger is more conservative

      -
      alpha
      +
      alpha

      L1 regularization on weights - larger is more conservative

      -
      seed
      +
      seed

      An option to add a seed when training the final model

      @@ -210,15 +208,15 @@

      Examples

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/setIterativeHardThresholding.html b/reference/setIterativeHardThresholding.html index 59765dedb..6a234802c 100644 --- a/reference/setIterativeHardThresholding.html +++ b/reference/setIterativeHardThresholding.html @@ -1,9 +1,9 @@ -Create setting for lasso logistic regression — setIterativeHardThresholding • PatientLevelPredictionCreate setting for lasso logistic regression — setIterativeHardThresholding • PatientLevelPrediction - +
      - +

      Create setting for lasso logistic regression

      - Source: R/CyclopsSettings.R + Source: R/CyclopsSettings.R
      @@ -149,49 +149,47 @@

      Create setting for lasso logistic regression

      Arguments

      - - -
      K
      +
      K

      The maximum number of non-zero predictors

      -
      penalty
      +
      penalty

      Specifies the IHT penalty; possible values are `BIC` or `AIC` or a numeric value

      -
      seed
      +
      seed

      An option to add a seed when training the model

      -
      exclude
      +
      exclude

      A vector of numbers or covariateId names to exclude from prior

      -
      forceIntercept
      +
      forceIntercept

      Logical: Force intercept coefficient into regularization

      -
      fitBestSubset
      +
      fitBestSubset

      Logical: Fit final subset with no regularization

      -
      initialRidgeVariance
      +
      initialRidgeVariance

      integer

      -
      tolerance
      +
      tolerance

      numeric

      -
      maxIterations
      +
      maxIterations

      integer

      -
      threshold
      +
      threshold

      numeric

      -
      delta
      +
      delta

      numeric

      @@ -213,15 +211,15 @@

      Examples

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/setKNN.html b/reference/setKNN.html index ae4db712b..e3025432b 100644 --- a/reference/setKNN.html +++ b/reference/setKNN.html @@ -1,9 +1,9 @@ -Create setting for knn model — setKNN • PatientLevelPredictionCreate setting for knn model — setKNN • PatientLevelPrediction - +
      - +

      Create setting for knn model

      - Source: R/KNN.R + Source: R/KNN.R
      @@ -137,26 +137,24 @@

      Create setting for knn model

      Arguments

      - - -
      k
      +
      k

      The number of neighbors to consider

      -
      indexFolder
      +
      indexFolder

      The directory where the results and intermediate steps are output

      -
      threads
      +
      threads

      The number of threads to use when applying big knn

      Examples

      - 
      if (FALSE) { # \dontrun{
+    
      if (FALSE) {
 model.knn <- setKNN(k=10000)
-} # }
+}
      @@ -171,15 +169,15 @@

      Examples

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/setLassoLogisticRegression.html b/reference/setLassoLogisticRegression.html index e3d1a291c..05f3b0a0b 100644 --- a/reference/setLassoLogisticRegression.html +++ b/reference/setLassoLogisticRegression.html @@ -1,9 +1,9 @@ -Create setting for lasso logistic regression — setLassoLogisticRegression • PatientLevelPredictionCreate setting for lasso logistic regression — setLassoLogisticRegression • PatientLevelPrediction - +
      - +

      Create setting for lasso logistic regression

      - Source: R/CyclopsSettings.R + Source: R/CyclopsSettings.R
      @@ -149,49 +149,47 @@

      Create setting for lasso logistic regression

      Arguments

      - - -
      variance
      +
      variance

      Numeric: prior distribution starting variance

      -
      seed
      +
      seed

      An option to add a seed when training the model

      -
      includeCovariateIds
      +
      includeCovariateIds

      a set of covariate IDS to limit the analysis to

      -
      noShrinkage
      +
      noShrinkage

      a set of covariates whcih are to be forced to be included in the final model. default is the intercept

      -
      threads
      +
      threads

      An option to set number of threads when training model

      -
      forceIntercept
      +
      forceIntercept

      Logical: Force intercept coefficient into prior

      -
      upperLimit
      +
      upperLimit

      Numeric: Upper prior variance limit for grid-search

      -
      lowerLimit
      +
      lowerLimit

      Numeric: Lower prior variance limit for grid-search

      -
      tolerance
      +
      tolerance

      Numeric: maximum relative change in convergence criterion from successive iterations to achieve convergence

      -
      maxIterations
      +
      maxIterations

      Integer: maximum iterations of Cyclops to attempt before returning a failed-to-converge error

      -
      priorCoefs
      +
      priorCoefs

      Use coefficients from a previous model as starting points for model fit (transfer learning)

      @@ -213,15 +211,15 @@

      Examples

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/setLightGBM.html b/reference/setLightGBM.html index e7c2a5f21..d15720ee7 100644 --- a/reference/setLightGBM.html +++ b/reference/setLightGBM.html @@ -1,9 +1,9 @@ -Create setting for gradient boosting machine model using lightGBM (https://github.com/microsoft/LightGBM/tree/master/R-package). — setLightGBM • PatientLevelPredictionCreate setting for gradient boosting machine model using lightGBM (https://github.com/microsoft/LightGBM/tree/master/R-package). — setLightGBM • PatientLevelPrediction - +
      - +

      Create setting for gradient boosting machine model using lightGBM (https://github.com/microsoft/LightGBM/tree/master/R-package).

      - Source: R/LightGBM.R + Source: R/LightGBM.R
      @@ -150,53 +150,51 @@

      Create setting for gradient boosting machine model using lightGBM (https://g

      Arguments

      - - -
      nthread
      +
      nthread

      The number of computer threads to use (how many cores do you have?)

      -
      earlyStopRound
      +
      earlyStopRound

      If the performance does not increase over earlyStopRound number of trees then training stops (this prevents overfitting)

      -
      numIterations
      +
      numIterations

      Number of boosting iterations.

      -
      numLeaves
      +
      numLeaves

      This hyperparameter sets the maximum number of leaves. Increasing this parameter can lead to higher model complexity and potential overfitting.

      -
      maxDepth
      +
      maxDepth

      This hyperparameter sets the maximum depth . Increasing this parameter can also lead to higher model complexity and potential overfitting.

      -
      minDataInLeaf
      +
      minDataInLeaf

      This hyperparameter sets the minimum number of data points that must be present in a leaf node. Increasing this parameter can help to reduce overfitting

      -
      learningRate
      +
      learningRate

      This hyperparameter controls the step size at each iteration of the gradient descent algorithm. Lower values can lead to slower convergence but may result in better performance.

      -
      lambdaL1
      +
      lambdaL1

      This hyperparameter controls L1 regularization, which can help to reduce overfitting by encouraging sparse models.

      -
      lambdaL2
      +
      lambdaL2

      This hyperparameter controls L2 regularization, which can also help to reduce overfitting by discouraging large weights in the model.

      -
      scalePosWeight
      +
      scalePosWeight

      Controls weight of positive class in loss - useful for imbalanced classes

      -
      isUnbalance
      +
      isUnbalance

      This parameter cannot be used at the same time with scalePosWeight, choose only one of them. While enabling this should increase the overall performance metric of your model, it will also result in poor estimates of the individual class probabilities.

      -
      seed
      +
      seed

      An option to add a seed when training the final model

      @@ -222,15 +220,15 @@

      Examples

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/setMLP.html b/reference/setMLP.html index 0976e808a..f7f77fb07 100644 --- a/reference/setMLP.html +++ b/reference/setMLP.html @@ -1,9 +1,9 @@ -Create setting for neural network model with python — setMLP • PatientLevelPredictionCreate setting for neural network model with python — setMLP • PatientLevelPrediction - +
      - +

      Create setting for neural network model with python

      - Source: R/SklearnClassifierSettings.R + Source: R/SklearnClassifierSettings.R
      @@ -159,13 +159,11 @@

      Create setting for neural network model with python

      Arguments

      - - -
      hiddenLayerSizes
      +
      hiddenLayerSizes

      (list of vectors) The ith element represents the number of neurons in the ith hidden layer.

      -
      activation
      +
      activation

      (list) Activation function for the hidden layer.

      • "identity": no-op activation, useful to implement linear bottleneck, returns f(x) = x

      • "logistic": the logistic sigmoid function, returns f(x) = 1 / (1 + exp(-x)).

      • "tanh": the hyperbolic tan function, returns f(x) = tanh(x).

        • @@ -173,88 +171,88 @@

        Arguments

      -
      solver
      +
      solver

      (list) The solver for weight optimization. (‘lbfgs’, ‘sgd’, ‘adam’)

      -
      alpha
      +
      alpha

      (list) L2 penalty (regularization term) parameter.

      -
      batchSize
      +
      batchSize

      (list) Size of minibatches for stochastic optimizers. If the solver is ‘lbfgs’, the classifier will not use minibatch. When set to “auto”, batchSize=min(200, n_samples).

      -
      learningRate
      +
      learningRate

      (list) Only used when solver='sgd' Learning rate schedule for weight updates. ‘constant’, ‘invscaling’, ‘adaptive’, default=’constant’

      -
      learningRateInit
      +
      learningRateInit

      (list) Only used when solver=’sgd’ or ‘adam’. The initial learning rate used. It controls the step-size in updating the weights.

      -
      powerT
      +
      powerT

      (list) Only used when solver=’sgd’. The exponent for inverse scaling learning rate. It is used in updating effective learning rate when the learning_rate is set to ‘invscaling’.

      -
      maxIter
      +
      maxIter

      (list) Maximum number of iterations. The solver iterates until convergence (determined by ‘tol’) or this number of iterations. For stochastic solvers (‘sgd’, ‘adam’), note that this determines the number of epochs (how many times each data point will be used), not the number of gradient steps.

      -
      shuffle
      +
      shuffle

      (list) boolean: Whether to shuffle samples in each iteration. Only used when solver=’sgd’ or ‘adam’.

      -
      tol
      +
      tol

      (list) Tolerance for the optimization. When the loss or score is not improving by at least tol for nIterNoChange consecutive iterations, unless learning_rate is set to ‘adaptive’, convergence is considered to be reached and training stops.

      -
      warmStart
      +
      warmStart

      (list) When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution.

      -
      momentum
      +
      momentum

      (list) Momentum for gradient descent update. Should be between 0 and 1. Only used when solver=’sgd’.

      -
      nesterovsMomentum
      +
      nesterovsMomentum

      (list) Whether to use Nesterov’s momentum. Only used when solver=’sgd’ and momentum > 0.

      -
      earlyStopping
      +
      earlyStopping

      (list) boolean Whether to use early stopping to terminate training when validation score is not improving. If set to true, it will automatically set aside 10 percent of training data as validation and terminate training when validation score is not improving by at least tol for n_iter_no_change consecutive epochs.

      -
      validationFraction
      +
      validationFraction

      (list) The proportion of training data to set aside as validation set for early stopping. Must be between 0 and 1. Only used if earlyStopping is True.

      -
      beta1
      +
      beta1

      (list) Exponential decay rate for estimates of first moment vector in adam, should be in 0 to 1.

      -
      beta2
      +
      beta2

      (list) Exponential decay rate for estimates of second moment vector in adam, should be in 0 to 1.

      -
      epsilon
      +
      epsilon

      (list) Value for numerical stability in adam.

      -
      nIterNoChange
      +
      nIterNoChange

      (list) Maximum number of epochs to not meet tol improvement. Only effective when solver=’sgd’ or ‘adam’.

      -
      seed
      +
      seed

      A seed for the model

      Examples

      - 
      if (FALSE) { # \dontrun{
+    
      if (FALSE) {
 model.mlp <- setMLP()
-} # }
+}
      @@ -269,15 +267,15 @@

      Examples

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/setNaiveBayes.html b/reference/setNaiveBayes.html index c2bf6bad9..a0389c1d8 100644 --- a/reference/setNaiveBayes.html +++ b/reference/setNaiveBayes.html @@ -1,9 +1,9 @@ -Create setting for naive bayes model with python — setNaiveBayes • PatientLevelPredictionCreate setting for naive bayes model with python — setNaiveBayes • PatientLevelPrediction - +
      - +

      Create setting for naive bayes model with python

      - Source: R/SklearnClassifierSettings.R + Source: R/SklearnClassifierSettings.R
      @@ -138,9 +138,9 @@

      Create setting for naive bayes model with python

      Examples

      - 
      if (FALSE) { # \dontrun{
+    
      if (FALSE) {
 model.nb <- setNaiveBayes()
-} # }
+}
      @@ -155,15 +155,15 @@

      Examples

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/setPythonEnvironment.html b/reference/setPythonEnvironment.html index 2256d7756..ce517c7b7 100644 --- a/reference/setPythonEnvironment.html +++ b/reference/setPythonEnvironment.html @@ -1,9 +1,9 @@ -Use the virtual environment created using configurePython() — setPythonEnvironment • PatientLevelPredictionUse the virtual environment created using configurePython() — setPythonEnvironment • PatientLevelPrediction - +
      - +

      Use the virtual environment created using configurePython()

      - Source: R/HelperFunctions.R + Source: R/HelperFunctions.R
      @@ -137,13 +137,11 @@

      Use the virtual environment created using configurePython()

      Arguments

      - - -
      envname
      +
      envname

      A string for the name of the virtual environment (default is 'PLP')

      -
      envtype
      +
      envtype

      An option for specifying the environment as'conda' or 'python'. If NULL then the default is 'conda' for windows users and 'python' for non-windows users

      @@ -164,15 +162,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/setRandomForest.html b/reference/setRandomForest.html index 26c8208e1..1b44fefc4 100644 --- a/reference/setRandomForest.html +++ b/reference/setRandomForest.html @@ -1,9 +1,9 @@ -Create setting for random forest model with python (very fast) — setRandomForest • PatientLevelPredictionCreate setting for random forest model with python (very fast) — setRandomForest • PatientLevelPrediction - +
      - +

      Create setting for random forest model with python (very fast)

      - Source: R/SklearnClassifierSettings.R + Source: R/SklearnClassifierSettings.R
      @@ -153,33 +153,31 @@

      Create setting for random forest model with python (very fast)

      Arguments

      - - -
      ntrees
      +
      ntrees

      (list) The number of trees to build

      -
      criterion
      +
      criterion

      (list) The function to measure the quality of a split. Supported criteria are “gini” for the Gini impurity and “entropy” for the information gain. Note: this parameter is tree-specific.

      -
      maxDepth
      +
      maxDepth

      (list) The maximum depth of the tree. If NULL, then nodes are expanded until all leaves are pure or until all leaves contain less than minSamplesSplit samples.

      -
      minSamplesSplit
      +
      minSamplesSplit

      (list) The minimum number of samples required to split an internal node

      -
      minSamplesLeaf
      +
      minSamplesLeaf

      (list) The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least minSamplesLeaf training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression.

      -
      minWeightFractionLeaf
      +
      minWeightFractionLeaf

      (list) The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sampleWeight is not provided.

      -
      mtries
      +
      mtries

      (list) The number of features to consider when looking for the best split:

      • int then consider max_features features at each split.

      • float then max_features is a fraction and round(max_features * n_features) features are considered at each split

      • 'sqrt' then max_features=sqrt(n_features)

        • @@ -188,45 +186,45 @@

        Arguments

      -
      maxLeafNodes
      +
      maxLeafNodes

      (list) Grow trees with max_leaf_nodes in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes.

      -
      minImpurityDecrease
      +
      minImpurityDecrease

      (list) A node will be split if this split induces a decrease of the impurity greater than or equal to this value.

      -
      bootstrap
      +
      bootstrap

      (list) Whether bootstrap samples are used when building trees. If False, the whole dataset is used to build each tree.

      -
      maxSamples
      +
      maxSamples

      (list) If bootstrap is True, the number of samples to draw from X to train each base estimator.

      -
      oobScore
      +
      oobScore

      (list) Whether to use out-of-bag samples to estimate the generalization score. Only available if bootstrap=True.

      -
      nJobs
      +
      nJobs

      The number of jobs to run in parallel.

      -
      classWeight
      +
      classWeight

      (list) Weights associated with classes. If not given, all classes are supposed to have weight one. NULL, “balanced”, “balanced_subsample”

      -
      seed
      +
      seed

      A seed when training the final model

      Examples

      - 
      if (FALSE) { # \dontrun{
+    
      if (FALSE) {
 model.rf <- setRandomForest(mtries=list('auto',5,20),  ntrees=c(10,100),
                            maxDepth=c(5,20))
-} # }
+}
      @@ -241,15 +239,15 @@

      Examples

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/setSVM.html b/reference/setSVM.html index 07db3bdff..c8ca9a9e7 100644 --- a/reference/setSVM.html +++ b/reference/setSVM.html @@ -1,9 +1,9 @@ -Create setting for the python sklearn SVM (SVC function) — setSVM • PatientLevelPredictionCreate setting for the python sklearn SVM (SVC function) — setSVM • PatientLevelPrediction - +
      - +

      Create setting for the python sklearn SVM (SVC function)

      - Source: R/SklearnClassifierSettings.R + Source: R/SklearnClassifierSettings.R
      @@ -148,54 +148,52 @@

      Create setting for the python sklearn SVM (SVC function)

      Arguments

      - - -
      C
      +
      C

      (list) Regularization parameter. The strength of the regularization is inversely proportional to C. Must be strictly positive. The penalty is a squared l2 penalty.

      -
      kernel
      +
      kernel

      (list) Specifies the kernel type to be used in the algorithm. one of ‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’. If none is given ‘rbf’ will be used.

      -
      degree
      +
      degree

      (list) degree of kernel function is significant only in poly, rbf, sigmoid

      -
      gamma
      +
      gamma

      (list) kernel coefficient for rbf and poly, by default 1/n_features will be taken. ‘scale’, ‘auto’ or float, default=’scale’

      -
      coef0
      +
      coef0

      (list) independent term in kernel function. It is only significant in poly/sigmoid.

      -
      shrinking
      +
      shrinking

      (list) whether to use the shrinking heuristic.

      -
      tol
      +
      tol

      (list) Tolerance for stopping criterion.

      -
      classWeight
      +
      classWeight

      (list) Class weight based on imbalance either 'balanced' or NULL

      -
      cacheSize
      +
      cacheSize

      Specify the size of the kernel cache (in MB).

      -
      seed
      +
      seed

      A seed for the model

      Examples

      - 
      if (FALSE) { # \dontrun{
+    
      if (FALSE) {
 model.svm <- setSVM(kernel='rbf', seed = NULL)
-} # }
+}
      @@ -210,15 +208,15 @@

      Examples

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/simulatePlpData.html b/reference/simulatePlpData.html index 33e8da3d5..28d042f5a 100644 --- a/reference/simulatePlpData.html +++ b/reference/simulatePlpData.html @@ -1,9 +1,9 @@ -Generate simulated data — simulatePlpData • PatientLevelPredictionGenerate simulated data — simulatePlpData • PatientLevelPrediction - +
      - +

      Generate simulated data

      - Source: R/Simulation.R + Source: R/Simulation.R
      @@ -137,20 +137,20 @@

      Generate simulated data

      Arguments

      - - -
      plpDataSimulationProfile
      +
      plpDataSimulationProfile

      An object of type plpDataSimulationProfile as generated using the
      createplpDataSimulationProfile function.

      -
      n
      +
      n

      The size of the population to be generated.

      Value

      -

      An object of type plpData.

      + + +

      An object of type plpData.

      Details

      @@ -171,15 +171,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/sklearnFromJson.html b/reference/sklearnFromJson.html index e42d57ed8..207925bfd 100644 --- a/reference/sklearnFromJson.html +++ b/reference/sklearnFromJson.html @@ -1,9 +1,9 @@ -Loads sklearn python model from json — sklearnFromJson • PatientLevelPredictionLoads sklearn python model from json — sklearnFromJson • PatientLevelPrediction - +
      - +

      Loads sklearn python model from json

      - Source: R/SklearnToJson.R + Source: R/SklearnToJson.R
      @@ -137,9 +137,7 @@

      Loads sklearn python model from json

      Arguments

      - - -
      path
      +
      path

      path to the model json file

      @@ -156,15 +154,15 @@

      Arguments

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/sklearnToJson.html b/reference/sklearnToJson.html index 7479a1e46..4f18ac540 100644 --- a/reference/sklearnToJson.html +++ b/reference/sklearnToJson.html @@ -1,9 +1,9 @@ -Saves sklearn python model object to json in path — sklearnToJson • PatientLevelPredictionSaves sklearn python model object to json in path — sklearnToJson • PatientLevelPrediction - +
      - +

      Saves sklearn python model object to json in path

      - Source: R/SklearnToJson.R + Source: R/SklearnToJson.R
      @@ -137,13 +137,11 @@

      Saves sklearn python model object to json in path

      Arguments

      - - -
      model
      +
      model

      a fitted sklearn python model object

      -
      path
      +
      path

      path to the saved model file

      @@ -160,15 +158,15 @@

      Arguments

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/specificity.html b/reference/specificity.html index 76b92d7c9..e16bf46a6 100644 --- a/reference/specificity.html +++ b/reference/specificity.html @@ -1,9 +1,9 @@ -Calculate the specificity — specificity • PatientLevelPredictionCalculate the specificity — specificity • PatientLevelPrediction - +
      - +

      Calculate the specificity

      - Source: R/ThresholdSummary.R + Source: R/ThresholdSummary.R
      @@ -137,27 +137,27 @@

      Calculate the specificity

      Arguments

      - - -
      TP
      +
      TP

      Number of true positives

      -
      TN
      +
      TN

      Number of true negatives

      -
      FN
      +
      FN

      Number of false negatives

      -
      FP
      +
      FP

      Number of false positives

      Value

      -

      specificity value

      + + +

      specificity value

      Details

      @@ -176,15 +176,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/splitData.html b/reference/splitData.html index a75063c6c..1b6baefdf 100644 --- a/reference/splitData.html +++ b/reference/splitData.html @@ -1,9 +1,9 @@ -Split the plpData into test/train sets using a splitting settings of class splitSettings — splitData • PatientLevelPredictionSplit the plpData into test/train sets using a splitting settings of class splitSettings — splitData • PatientLevelPrediction - +
      - +

      Split the plpData into test/train sets using a splitting settings of class splitSettings

      - Source: R/DataSplitting.R + Source: R/DataSplitting.R
      @@ -141,24 +141,26 @@

      Split the plpData into test/train sets using a splitting settings of class <

      Arguments

      - - -
      plpData
      +
      plpData

      An object of type plpData - the patient level prediction data extracted from the CDM.

      -
      population
      +
      population

      The population created using createStudyPopulation that define who will be used to develop the model

      -
      splitSettings
      +
      splitSettings

      An object of type splitSettings specifying the split - the default can be created using createDefaultSplitSetting

      Value

      -

      An object of class splitSettings

      + + +

      An object of class splitSettings

      + +

      Details

      @@ -181,15 +183,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/toSparseM.html b/reference/toSparseM.html index 120ed44bf..7c240da30 100644 --- a/reference/toSparseM.html +++ b/reference/toSparseM.html @@ -1,9 +1,9 @@ -Convert the plpData in COO format into a sparse R matrix — toSparseM • PatientLevelPredictionConvert the plpData in COO format into a sparse R matrix — toSparseM • PatientLevelPrediction - +
      - +

      Convert the plpData in COO format into a sparse R matrix

      - Source: R/Formatting.R + Source: R/Formatting.R
      @@ -137,24 +137,24 @@

      Convert the plpData in COO format into a sparse R matrix

      Arguments

      - - -
      plpData
      +
      plpData

      An object of type plpData with covariate in coo format - the patient level prediction data extracted from the CDM.

      -
      cohort
      +
      cohort

      If specified the plpData is restricted to the rowIds in the cohort (otherwise plpData$labels is used)

      -
      map
      +
      map

      A covariate map (telling us the column number for covariates)

      Value

      -

      Returns a list, containing the data as a sparse matrix, the plpData covariateRef + + +

      Returns a list, containing the data as a sparse matrix, the plpData covariateRef and a data.frame named map that tells us what covariate corresponds to each column This object is a list with the following components:

      data

      A sparse matrix with the rows corresponding to each person in the plpData and the columns corresponding to the covariates.

      @@ -192,15 +192,15 @@

      Examples

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/validateExternal.html b/reference/validateExternal.html index c3958ad02..ce79f9c15 100644 --- a/reference/validateExternal.html +++ b/reference/validateExternal.html @@ -1,9 +1,9 @@ -externalValidatePlp - Validate model performance on new data — validateExternal • PatientLevelPredictionexternalValidatePlp - Validate model performance on new data — validateExternal • PatientLevelPrediction - +
      - +

      externalValidatePlp - Validate model performance on new data

      - Source: R/ExternalValidatePlp.R + Source: R/ExternalValidatePlp.R
      @@ -142,23 +142,21 @@

      externalValidatePlp - Validate model performance on new data

      Arguments

      - - -
      validationDesignList
      +
      validationDesignList

      A list of objects created with createValidationDesign

      -
      databaseDetails
      +
      databaseDetails

      A list of objects of class databaseDetails created using createDatabaseDetails

      -
      logSettings
      +
      logSettings

      An object of logSettings created using createLogSettings

      -
      outputFolder
      +
      outputFolder

      The directory to save the validation results to (subfolders are created per database in validationDatabaseDetails)

      @@ -176,15 +174,15 @@

      Arguments

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/validateMultiplePlp.html b/reference/validateMultiplePlp.html index 8aa20b887..c75da7891 100644 --- a/reference/validateMultiplePlp.html +++ b/reference/validateMultiplePlp.html @@ -1,10 +1,10 @@ -externally validate the multiple plp models across new datasets — validateMultiplePlp • PatientLevelPredictionexternally validate the multiple plp models across new datasets — validateMultiplePlp • PatientLevelPrediction - +
      - +

      externally validate the multiple plp models across new datasets

      - Source: R/RunMultiplePlp.R + Source: R/RunMultiplePlp.R
      @@ -146,29 +146,27 @@

      externally validate the multiple plp models across new datasets

      Arguments

      - - -
      analysesLocation
      +
      analysesLocation

      The location where the multiple plp analyses are

      -
      validationDatabaseDetails
      +
      validationDatabaseDetails

      A single or list of validation database settings created using createDatabaseDetails()

      -
      validationRestrictPlpDataSettings
      +
      validationRestrictPlpDataSettings

      The settings specifying the extra restriction settings when extracting the data created using createRestrictPlpDataSettings().

      -
      recalibrate
      +
      recalibrate

      A vector of recalibration methods (currently supports 'RecalibrationintheLarge' and/or 'weakRecalibration')

      -
      cohortDefinitions
      +
      cohortDefinitions

      A list of cohortDefinitions

      -
      saveDirectory
      +
      saveDirectory

      The location to save to validation results

      @@ -190,15 +188,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/viewDatabaseResultPlp.html b/reference/viewDatabaseResultPlp.html index d52f2e864..6ff722a2e 100644 --- a/reference/viewDatabaseResultPlp.html +++ b/reference/viewDatabaseResultPlp.html @@ -1,9 +1,9 @@ -open a local shiny app for viewing the result of a PLP analyses from a database — viewDatabaseResultPlp • PatientLevelPredictionopen a local shiny app for viewing the result of a PLP analyses from a database — viewDatabaseResultPlp • PatientLevelPrediction - +
      - +

      open a local shiny app for viewing the result of a PLP analyses from a database

      - Source: R/ViewShinyPlp.R + Source: R/ViewShinyPlp.R
      @@ -145,33 +145,31 @@

      open a local shiny app for viewing the result of a PLP analyses from a datab

      Arguments

      - - -
      mySchema
      +
      mySchema

      Database result schema containing the result tables

      -
      myServer
      +
      myServer

      server with the result database

      -
      myUser
      +
      myUser

      Username for the connection to the result database

      -
      myPassword
      +
      myPassword

      Password for the connection to the result database

      -
      myDbms
      +
      myDbms

      database management system for the result database

      -
      myPort
      +
      myPort

      Port for the connection to the result database

      -
      myTableAppend
      +
      myTableAppend

      A string appended to the results tables (optional)

      @@ -192,15 +190,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/viewMultiplePlp.html b/reference/viewMultiplePlp.html index 72ab7c3e9..60cbf1a87 100644 --- a/reference/viewMultiplePlp.html +++ b/reference/viewMultiplePlp.html @@ -1,9 +1,9 @@ -open a local shiny app for viewing the result of a multiple PLP analyses — viewMultiplePlp • PatientLevelPredictionopen a local shiny app for viewing the result of a multiple PLP analyses — viewMultiplePlp • PatientLevelPrediction - +
      - +

      open a local shiny app for viewing the result of a multiple PLP analyses

      - Source: R/ViewShinyPlp.R + Source: R/ViewShinyPlp.R
      @@ -137,9 +137,7 @@

      open a local shiny app for viewing the result of a multiple PLP analyses

      Arguments

      - - -
      analysesLocation
      +
      analysesLocation

      The directory containing the results (with the analysis_x folders)

      @@ -161,15 +159,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/reference/viewPlp.html b/reference/viewPlp.html index f926048fe..5dc710218 100644 --- a/reference/viewPlp.html +++ b/reference/viewPlp.html @@ -1,9 +1,9 @@ -viewPlp - Interactively view the performance and model settings — viewPlp • PatientLevelPredictionviewPlp - Interactively view the performance and model settings — viewPlp • PatientLevelPrediction - +
      - +

      viewPlp - Interactively view the performance and model settings

      - Source: R/ViewShinyPlp.R + Source: R/ViewShinyPlp.R
      @@ -137,23 +137,23 @@

      viewPlp - Interactively view the performance and model settings

      Arguments

      - - -
      runPlp
      +
      runPlp

      The output of runPlp() (an object of class 'runPlp')

      -
      validatePlp
      +
      validatePlp

      The output of externalValidatePlp (on object of class 'validatePlp')

      -
      diagnosePlp
      +
      diagnosePlp

      The output of diagnosePlp()

      Value

      -

      Opens a shiny app for interactively viewing the results

      + + +

      Opens a shiny app for interactively viewing the results

      Details

      @@ -172,15 +172,15 @@

      Details

      -

      Site built with pkgdown 2.1.0.

      +

      Site built with pkgdown 2.0.7.

      + - - + diff --git a/sitemap.xml b/sitemap.xml index 096ed2cfc..5c677f919 100644 --- a/sitemap.xml +++ b/sitemap.xml @@ -1,154 +1,456 @@ - -/404.html -/articles/AddingCustomFeatureEngineering.html -/articles/AddingCustomModels.html -/articles/AddingCustomSamples.html -/articles/AddingCustomSplitting.html -/articles/BenchmarkTasks.html -/articles/BestPractices.html -/articles/BuildingMultiplePredictiveModels.html -/articles/BuildingPredictiveModels.html -/articles/ClinicalModels.html -/articles/ConstrainedPredictors.html -/articles/CreatingLearningCurves.html -/articles/CreatingNetworkStudies.html -/articles/InstallationGuide.html -/articles/Videos.html -/articles/index.html -/authors.html -/index.html -/news/index.html -/reference/MapIds.html -/reference/PatientLevelPrediction.html -/reference/accuracy.html -/reference/addDiagnosePlpToDatabase.html -/reference/addMultipleDiagnosePlpToDatabase.html -/reference/addMultipleRunPlpToDatabase.html -/reference/addRunPlpToDatabase.html -/reference/averagePrecision.html -/reference/brierScore.html -/reference/calibrationLine.html -/reference/computeAuc.html -/reference/computeGridPerformance.html -/reference/configurePython.html -/reference/covariateSummary.html -/reference/createCohortCovariateSettings.html -/reference/createDatabaseDetails.html -/reference/createDatabaseList.html -/reference/createDatabaseSchemaSettings.html -/reference/createDefaultExecuteSettings.html -/reference/createDefaultSplitSetting.html -/reference/createExecuteSettings.html -/reference/createFeatureEngineeringSettings.html -/reference/createLearningCurve.html -/reference/createLogSettings.html -/reference/createModelDesign.html -/reference/createPlpResultTables.html -/reference/createPreprocessSettings.html -/reference/createRandomForestFeatureSelection.html -/reference/createRestrictPlpDataSettings.html -/reference/createSampleSettings.html -/reference/createSplineSettings.html -/reference/createStratifiedImputationSettings.html 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