glm.best: Title: Fitting generalized linear models for the best model
In VariableSelection: Select Variables for Linear Models

View source: R/fit_glm.R

glm.best

R Documentation

Title: Fitting generalized linear models for the best model

Description

Description: glm.best is used to fit generalized linear model for the best model provided by modelselect.glm.

Usage

glm.best(
  object,
  family,
  method = "models",
  threshold = 0.95,
  x = FALSE,
  y = FALSE
)

Arguments

`object`	the model selection result from `modelselect.glm`.
`family`	a character string naming a family function describing the error distribution to be used in the model.
`method`	the criteria to do model select. `method = "models"` selects the best model by the highest posterior probabilities. `method = "variables"` selects the variables in the best model by the posterior inclusion probabilities which are larger than the threshold.
`threshold`	The threshold for variable selection. The variables with posterior inclusion probability larger than the threshold are selected in the best model. The default is 0.95.
`x`, `y`	logicals. If `TRUE` the corresponding components (the best model predictor matrix, the response) of the fit are returned.

Value

An object of class "glm", which is a list containing the following components:

coefficients: a named vector of coefficients.
residuals: the working residuals, that is the residuals in the final iteration of the IWLS fit.
fitted.values: the fitted mean values, obtained by transforming the linear predictors by the inverse of the link function.
rank: the numeric rank of the fitted linear model.
family: the family object used.
linear.predictors: the linear fit on the link scale.
deviance: up to a constant, minus twice the maximized log-likelihood.
aic: A version of Akaike's An Information Criterion, minus twice the maximized log-likelihood plus twice the number of parameters, computed by the aic component of the family.
null.deviance: The deviance for the null model, comparable with deviance. The null model will include the offset, and an intercept if there is one in the model.
iter: the number of iterations of IWLS used.
weights: the working weights, that is the weights in the final iteration of the IWLS fit.
prior.weights: the weights initially supplied, a vector of 1s if none were.
df.residual: the residual degrees of freedom.
df.null: the residual degrees of freedom for the null model.
y: if requested, the response vector used.
converged: logical. Was the IWLS algorithm judged to have converged?
boundary: logical. Is the fitted value on the boundary of the allowable values?
model: if requested (the default), the model frame used.
call: the matched call.
formula: the formula supplied.
terms: the terms.object used.
data: the data argument.
threshold: the threshold used for method = "variables".