covariateSummary {PatientLevelPrediction} | R Documentation |
covariateSummary
Description
Summarises the covariateData to calculate the mean and standard deviation per covariate if the labels are given it also stratifies this by class label and if the trainRowIds and testRowIds specifying the patients in the train/test sets respectively are input, these values are also stratified by train and test set
Usage
covariateSummary(
covariateData,
cohort,
labels = NULL,
strata = NULL,
variableImportance = NULL,
featureEngineering = NULL
)
Arguments
covariateData |
The covariateData part of the plpData that is
extracted using |
cohort |
The patient cohort to calculate the summary |
labels |
A data.frame with the columns rowId and outcomeCount |
strata |
A data.frame containing the columns rowId, strataName |
variableImportance |
A data.frame with the columns covariateId and value (the variable importance value) |
featureEngineering |
(currently not used ) A function or list of functions specifying any feature engineering to create covariates before summarising |
Details
The function calculates various metrics to measure the performance of the model
Value
A data.frame containing: CovariateCount, CovariateMean and CovariateStDev for any specified stratification
Examples
data("simulationProfile")
plpData <- simulatePlpData(simulationProfile, n = 100)
covariateSummary <- covariateSummary(plpData$covariateData, plpData$cohorts)
head(covariateSummary)