Create modelSettings for ridge logistic regression

Usage

setRidgeRegression(
  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: A data frame with covariateIds and betas from a previous model. Numeric and character covariate IDs are matched by value. Supply IDs at or above 2^53 as full integer strings. Coefficients must use the same covariate scaling as the training data. Changes from these coefficients are penalized; the intercept is estimated separately and any supplied intercept is ignored. includeCovariateIds also applies to these coefficients. Source coefficients for covariates absent from the target training data are dropped, and their count is logged.

Value

modelSettings object

Examples

modelRidge <- setRidgeRegression(seed = 42)