fitPlp

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

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 function:

• setLassoLogisticRegression() A lasso logistic regression model

• setGradientBoostingMachine() A gradient boosting machine

• setRandomForest() A random forest model

• setKNN() A KNN model

search

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

analysisId

The id of the analysis

analysisPath

The path of the analysis

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

Details

The user can define the machine learning model to train (regularised logistic regression, random forest, gradient boosting machine, neural network and )