Create modelSettings for lasso logistic regression

Usage

setLassoLogisticRegression(
  variance = 0.01,
  seed = NULL,
  includeCovariateIds = c(),
  noShrinkage = c(0),
  threads = -1,
  forceIntercept = FALSE,
  upperLimit = 20,
  lowerLimit = 0.01,
  tolerance = 2e-06,
  maxIterations = 3000,
  priorCoefs = NULL
)

Arguments

variance: Numeric: prior distribution starting variance
seed: An option to add a seed when training the model
includeCovariateIds: a set of covariateIds to limit the analysis to
noShrinkage: a set of covariates whcih are to be forced to be included in in the final model. Default is the intercept
threads: An option to set number of threads when training model.
forceIntercept: Logical: Force intercept coefficient into prior
upperLimit: Numeric: Upper prior variance limit for grid-search
lowerLimit: Numeric: Lower prior variance limit for grid-search
tolerance: Numeric: maximum relative change in convergence criterion from from successive iterations to achieve convergence
maxIterations: Integer: maximum iterations of Cyclops to attempt before returning a failed-to-converge error
priorCoefs: Use coefficients from a previous model as starting points for model fit (transfer learning)

Value

modelSettings object

Examples

modelLasso <- setLassoLogisticRegression(seed=42)