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.

Value

An object of class preprocessingSettings

Details

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

Examples

# Create the settings for preprocessing, remove no features, normalise the data
createPreprocessSettings(minFraction = 0.0, normalize = TRUE, removeRedundancy = FALSE)
#> $minFraction
#> [1] 0
#> 
#> $normalize
#> [1] TRUE
#> 
#> $removeRedundancy
#> [1] FALSE
#> 
#> attr(,"class")
#> [1] "preprocessSettings"