Create setting for lasso logistic regression

Create setting for lasso logistic regression

setLassoLogisticRegression(
  variance = 0.01,
  seed = NULL,
  includeCovariateIds = c(),
  noShrinkage = c(0),
  threads = -1,
  forceIntercept = F,
  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 covariate IDS to limit the analysis to

noShrinkage

a set of covariates whcih are to be forced to be included 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 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)

Examples

model.lr <- setLassoLogisticRegression()