setIterativeHardThresholding {PatientLevelPrediction} | R Documentation |
Create setting for Iterative Hard Thresholding model
Description
Create setting for Iterative Hard Thresholding model
Usage
setIterativeHardThresholding(
K = 10,
penalty = "bic",
seed = sample(1e+05, 1),
exclude = c(),
forceIntercept = FALSE,
fitBestSubset = FALSE,
initialRidgeVariance = 0.1,
tolerance = 1e-08,
maxIterations = 10000,
threshold = 1e-06,
delta = 0
)
Arguments
K |
The maximum number of non-zero predictors |
penalty |
Specifies the IHT penalty; possible values are |
seed |
An option to add a seed when training the model |
exclude |
A vector of numbers or covariateId names to exclude from prior |
forceIntercept |
Logical: Force intercept coefficient into regularization |
fitBestSubset |
Logical: Fit final subset with no regularization |
initialRidgeVariance |
integer |
tolerance |
numeric |
maxIterations |
integer |
threshold |
numeric |
delta |
numeric |
Value
modelSettings
object
Examples
modelIht <- setIterativeHardThresholding(K = 5, seed = 42)
[Package PatientLevelPrediction version 6.4.1 Index]