fitPlp: fitPlp

In OHDSI/PatientLevelPrediction: Developing patient level prediction using data in the OMOP Common Data Model

fitPlp

Description

Train various models using a default parameter gird 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 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

Details

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

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

OHDSI/PatientLevelPrediction documentation built on Sept. 15, 2024, 6:38 a.m.