fglm: Forward Regression Selection for Generalized Linear Models
In pboost: Profile Boosting Framework for Parametric Models

View source: R/fglm.R

fglm	R Documentation

Forward Regression Selection for Generalized Linear Models

Description

fglm() inherits the usage of glm, and performs forward regression selection for generalized linear models.

Usage

fglm(
  formula,
  family = gaussian,
  data,
  weights,
  subset,
  na.action,
  start = NULL,
  etastart,
  mustart,
  offset,
  control = list(...),
  model = TRUE,
  method = "glm.fit",
  x = FALSE,
  y = TRUE,
  singular.ok = TRUE,
  contrasts = NULL,
  ...,
  selectFun = logLik,
  stopFun = "EBIC",
  keep = NULL,
  maxK = NULL,
  verbose = FALSE
)

fglm.fit(
  x,
  y,
  weights = rep.int(1, NROW(y)),
  start = NULL,
  etastart = NULL,
  mustart = NULL,
  offset = rep.int(0, NROW(y)),
  family = gaussian(),
  control = list(),
  intercept = TRUE,
  singular.ok = TRUE,
  selectFun = "logLik",
  stopFun = "EBIC",
  keep = NULL,
  maxK = NULL,
  verbose = FALSE
)

Arguments

`formula`	Parameter passed to glm.
`family`	Parameter passed to glm.
`data`	Parameter passed to glm.
`weights`	Parameter passed to glm.
`subset`	Parameter passed to glm.
`na.action`	Parameter passed to glm.
`start`	Parameter passed to glm.
`etastart`	Parameter passed to glm.
`mustart`	Parameter passed to glm.
`offset`	Parameter passed to glm.
`control`	Parameter passed to glm.
`model`	Parameter passed to glm.
`method`	Parameter passed to glm.
`x`	Parameter passed to glm.
`y`	Parameter passed to glm.
`singular.ok`	Parameter passed to glm.
`contrasts`	Parameter passed to glm.
`...`	Parameters passed to glm.
`selectFun`	Parameter passed to frs.
`stopFun`	Parameter passed to frs.
`keep`	Parameter passed to frs.
`maxK`	Parameter passed to frs.
`verbose`	Parameter passed to frs.
`intercept`	Parameter passed to glm.fit.