fitPlp: fitPlp

In OHDSI/PatientLevelPrediction: Develop Clinical Prediction Models Using the Common Data Model

fitPlp

Description

Train various models using a default parameter grid search or user specified parameters

Usage

fitPlp(trainData, modelSettings, search = "grid", analysisId, analysisPath)

Arguments

trainData	An object of type trainData created using splitData data extracted from the CDM.
modelSettings	An object of class modelSettings created using one of the createModelSettings functions
search	The search strategy for the hyper-parameter selection (currently not used)
analysisId	The id of the analysis
analysisPath	The path of the analysis

Details

The user can define the machine learning model to train

Value

An object of class plpModel containing:

model	The trained prediction model
preprocessing	The preprocessing required when applying the model
prediction	The cohort data.frame with the predicted risk column added
modelDesign	A list specifiying the modelDesign settings used to fit the model
trainDetails	The model meta data
covariateImportance	The covariate importance for the model

Examples

 
# simulate data
data("simulationProfile")
plpData <- simulatePlpData(simulationProfile, n=1000)
# create study population, split into train/test and preprocess with default settings
population <- createStudyPopulation(plpData, outcomeId = 3)
data <- splitData(plpData, population, createDefaultSplitSetting())
data$Train$covariateData <- preprocessData(data$Train$covariateData)
saveLoc <- file.path(tempdir(), "fitPlp")
# fit a lasso logistic regression model using the training data
plpModel <- fitPlp(data$Train, modelSettings=setLassoLogisticRegression(seed=42),
                   analysisId=1, analysisPath=saveLoc)
# show evaluationSummary for model
evaluatePlp(plpModel$prediction)$evaluationSummary
# clean up
unlink(saveLoc, recursive = TRUE)

OHDSI/PatientLevelPrediction documentation built on Feb. 14, 2025, 9:44 a.m.