PatientLevelPrediction (version 6.4.0)

setIterativeHardThresholding: 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
)

Value

modelSettings object

Arguments

K

The maximum number of non-zero predictors

penalty

Specifies the IHT penalty; possible values are BIC or AIC or a numeric value

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

Examples

if (FALSE) { # rlang::is_installed("IterativeHardThresholding")
modelIht <- setIterativeHardThresholding(K = 5, seed = 42)
}