fitPlp: fitPlp

In quinterpriest/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)

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: logisticRegressionModel() A lasso logistic regression model GBMclassifier() A gradient boosting machine RFclassifier() A random forest model GLMclassifier () A generalised linear model KNNclassifier() A KNN model
search	The search strategy for the hyper-parameter selection (currently not used)
analysisId	The id 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
modelLoc	The path to where the model is saved (if saved)
trainAuc	The AUC obtained on the training set
trainCalibration	The calibration obtained on the training set
modelSettings	A list specifiying the model, preprocessing, outcomeId and cohortId
metaData	The model meta data
trainingTime	The time taken to train the classifier

quinterpriest/PatientLevelPrediction documentation built on April 20, 2022, 12:50 a.m.