fitPlp: fitPlp
In ted9219/CoDImputationHeart: Cause of death imputation package

Description Usage Arguments Details Value

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

fitPlp(
  population,
  data,
  modelSettings,
  cohortId,
  outcomeId,
  minCovariateFraction = 0.001,
  normalizeData = T
)

`population`	The population created using createStudyPopulation() who will have their risks predicted
`data`	An object of type `plpData` - the patient level prediction 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
`cohortId`	Id of study cohort
`outcomeId`	Id of outcome cohort
`minCovariateFraction`	The minimum fraction of the target popualtion who have a variable for it to be included in the model training
`normalizeData`	Whether to normalise the data before model fitting

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

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