preprocessData {PatientLevelPrediction} | R Documentation |
A function that wraps around FeatureExtraction::tidyCovariateData to normalise the data and remove rare or redundant features
Description
A function that wraps around FeatureExtraction::tidyCovariateData to normalise the data and remove rare or redundant features
Usage
preprocessData(covariateData, preprocessSettings = createPreprocessSettings())
Arguments
covariateData |
The covariate part of the training data created by |
preprocessSettings |
The settings for the preprocessing created by |
Details
Returns an object of class covariateData
that has been processed.
This includes normalising the data and removing rare or redundant features.
Redundant features are features that within an analysisId together cover
all obervations.
Value
The covariateData object with the processed covariates
Examples
library(dplyr)
data("simulationProfile")
plpData <- simulatePlpData(simulationProfile, n=1000)
preProcessedData <- preprocessData(plpData$covariateData, createPreprocessSettings())
# check age is normalized by max value
preProcessedData$covariates %>% dplyr::filter(.data$covariateId == 1002)
[Package PatientLevelPrediction version 6.4.1 Index]