setIterativeHardThresholding: Create setting for lasso logistic regression

In OHDSI/PatientLevelPrediction: Developing patient level prediction using data in the OMOP Common Data Model

Create setting for lasso logistic regression

Description

Create setting for lasso logistic regression

Usage

setIterativeHardThresholding(
  K = 10,
  penalty = "bic",
  seed = sample(1e+05, 1),
  exclude = c(),
  forceIntercept = F,
  fitBestSubset = FALSE,
  initialRidgeVariance = 10000,
  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

Examples

model.lr <- setLassoLogisticRegression()

OHDSI/PatientLevelPrediction documentation built on Sept. 15, 2024, 6:38 a.m.