setIterativeHardThresholding: Create setting for Iterative Hard Thresholding model

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

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 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

Value

modelSettings object

Examples

modelIht <- setIterativeHardThresholding(K = 5, seed = 42)

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