glmtoolbox: stepCriterion.overglm – R documentation

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glmtoolbox

stepCriterion.overglm

Variable selection in Negative Binomial and Beta-Binomial Regression Models

Description

Performs variable selection in negative binomial and beta-binomial regression models.

Usage

## S3 method for class 'overglm'
stepCriterion(
  model,
  criterion = c("bic", "aic", "p-value"),
  direction = c("backward", "forward"),
  test = c("wald", "score", "lrt", "gradient"),
  level = 0.05,
  verbose = TRUE,
  ...
)

Arguments

`model`	an object of the class overglm which is obtained from the fit of a negative binomial or beta-binomial model. The linear predictor of the model whose fit is stored in this overglm object is the more complex candidate which should be considered by the variable selection procedure.
`criterion`	an (optional) character string indicating the criterion which should be used to compare the candidate models. The available options are: AIC ("aic"), BIC ("bic") and p-value of the `test` test ("p-value"). By default, `criterion` is set to be "bic".
`direction`	an (optional) character string indicating the mode of variable selection which should be used. The available options are: deleting variables ("backward") and adding variables ("forward"). By default, `direction` is set to be "backward".
`test`	an (optional) character string indicating the statistical test which should be used to compare nested models. The available options are: Wald ("wald"), Rao's score ("score"), likelihood ratio ("lrt") and gradient ("gradient") tests. By default, `test` is set to be "wald".
`level`	an (optional) numeric value in the interval (0,1) indicating the significance level chosen to perform the statistical tests. This is only appropiate if `criterion="p-value"`. By default, `level` is set to be 0.05.
`verbose`	an (optional) logical switch indicating if should the report of results be printed. By default, `verbose` is set to be TRUE.
`...`	further arguments passed to or from other methods. For example, `k`, that is, the magnitude of the penalty in the AIC, which by default is set to be 2.

Value

A list which contains the following objects:

initial: an expression describing the linear predictor of the "initial" model.
final: an expression describing the linear predictor of the "final" model.
criterion: a character string describing the criterion chosen to compare the candidate models.