models: Extract Binary Logit Models from a 'nestedLogit' Object

In nestedLogit: Nested Dichotomy Logistic Regression Models

Extract Binary Logit Models from a nestedLogit Object

Description

models is used to extract "glm" objects representing binary logit models from a "nestedLogit" object.

Usage

models(model, select, as.list = FALSE)

## S3 method for class 'nestedLogit'
models(model, select, as.list = FALSE)

Arguments

model	a "nestedLogit" model.
select	a numeric or character vector giving the number(s) or names(s) of one or more binary logit models to be extracted from model; if absent, a list of all of the binary logits models in model is returned.
as.list	if TRUE (the default is FALSE) and one binary logit model is selected, return the "glm" object in a one-element named list; otherwise a single model is returned directly as a "glm" object; when more than one binary logit model is selected, the corresponding "glm" objects are always returned as a named list.

Value

model returns either a single "glm" object (see glm) or a list of "glm" objects, each representing a binary logit model.

Examples

  data("Womenlf", package = "carData")
  comparisons <- logits(work=dichotomy("not.work",
                                       working=c("parttime", "fulltime")),
                        full=dichotomy("parttime", "fulltime"))
  m <- nestedLogit(partic ~ hincome + children,
                   dichotomies = comparisons,
                   data=Womenlf)

  # extract a binomial logit model
  models(m, "work")
  # use that to plot residuals
  plot(density(residuals(models(m, "work"))))

nestedLogit documentation built on July 9, 2023, 6:35 p.m.