banditGlmnet: Estimate a banditGlmnet model
In rferrali/banditr: Estimation of Multi-Armed Bandit Algorithms

Description Usage Arguments Value

The syntax follows closely that of glm. All parameters not defined on this page are defined as in glm.

banditGlmnet(formula, family = c("gaussian", "binomial"), lambdaRidge,
  lambdaLasso, data, weights, subset, na.action, offset, seed = NULL,
  parRidge = NULL, parLasso = NULL, model = TRUE, method = "glmnet",
  x = FALSE, y = TRUE, contrasts = NULL)

`family`	supported response type. Currently, either `"gaussian"` (the default) or `"binomial"`.
`lambdaRidge`	a scalar in [0,1]. Tuning parameter of the ridge regression.
`lambdaLasso`	a scalar in [0,1]. Tuning parameter of the LASSO.
`seed`	an (optional) seeding number.
`parRidge`	an (optional) list of parameters passed on to glmnet for the ridge stage. Format argument = value.
`parLasso`	an (optional) list of parameters passed on to glmnet for the LASSO stage. Format argument = value
`method`	the method to be used in fitting the model. The default `"glmnet"` uses `glmnet`; the alternative `"model.frame"` returns the model frame and does no fitting.