preprocessData: A function that wraps around...

In OHDSI/PatientLevelPrediction: Develop Clinical Prediction Models Using the Common Data Model

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 splitData after being sampled and having any required feature engineering
preprocessSettings	The settings for the preprocessing created by createPreprocessSettings The data processed

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)

OHDSI/PatientLevelPrediction documentation built on Feb. 14, 2025, 9:44 a.m.