createIterativeImputer {PatientLevelPrediction} | R Documentation |
Create Iterative Imputer settings
Description
This function creates the settings for an iterative imputer
which first removes features with more than missingThreshold
missing values
and then imputes the missing values iteratively using chained equations
Usage
createIterativeImputer(
missingThreshold = 0.3,
method = "pmm",
methodSettings = list(pmm = list(k = 5, iterations = 5))
)
Arguments
missingThreshold |
The threshold for missing values to remove a feature |
method |
The method to use for imputation, currently only "pmm" is supported |
methodSettings |
A list of settings for the imputation method to use. Currently only "pmm" is supported with the following settings:
|
Value
The settings for the iterative imputer of class featureEngineeringSettings
Examples
# create imputer to impute values with missingness less than 30% using
# predictive mean matching in 5 iterations with 5 donors
createIterativeImputer(missingThreshold = 0.3, method = "pmm",
methodSettings = list(pmm = list(k = 5, iterations = 5)))
[Package PatientLevelPrediction version 6.4.1 Index]