PatientLevelPrediction (version 6.4.0)

covariateSummary: 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
)

Value

A data.frame containing: CovariateCount, CovariateMean and CovariateStDev for any specified stratification

Arguments

covariateData

The covariateData part of the plpData that is extracted using getPlpData

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

Examples

data("simulationProfile")
plpData <- simulatePlpData(simulationProfile, n=100)
covariateSummary <- covariateSummary(plpData$covariateData, plpData$cohorts)
head(covariateSummary)