Create setting for lasso logistic regression

Create setting for lasso logistic regression

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()