createPreprocessSettings: Create the settings for preprocessing the trainData.

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

Create the settings for preprocessing the trainData.

Description

Create the settings for preprocessing the trainData.

Usage

createPreprocessSettings(
  minFraction = 0.001,
  normalize = TRUE,
  removeRedundancy = TRUE
)

Arguments

minFraction	The minimum fraction of target population who must have a covariate for it to be included in the model training
normalize	Whether to normalise the covariates before training (Default: TRUE)
removeRedundancy	Whether to remove redundant features (Default: TRUE) Redundant features are features that within an analysisId together cover all observations. For example with ageGroups, if you have ageGroup 0-18 and 18-100 and all patients are in one of these groups, then one of these groups is redundant.

Details

Returns an object of class preprocessingSettings that specifies how to preprocess the training data

Value

An object of class preprocessingSettings

Examples

# Create the settings for preprocessing, remove no features, normalise the data
createPreprocessSettings(minFraction = 0.0, normalize = TRUE, removeRedundancy = FALSE)

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