mclogit: mblogit – R documentation

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mblogit

Baseline-Category Logit Models for Categorical and Multinomial Responses

Description

The function mblogit fits baseline-category logit models for categorical and multinomial count responses with fixed alternatives.

Usage

mblogit(
  formula,
  data = parent.frame(),
  random = NULL,
  subset,
  weights = NULL,
  na.action = getOption("na.action"),
  model = TRUE,
  x = FALSE,
  y = TRUE,
  contrasts = NULL,
  method = NULL,
  estimator = c("ML", "REML"),
  dispersion = FALSE,
  from.table = FALSE,
  groups = NULL,
  control = if (length(random)) mmclogit.control(...) else mclogit.control(...),
  ...
)

Arguments

`formula`	the model formula. The response must be a factor or a matrix of counts.
`data`	an optional data frame, list or environment (or object coercible by `as.data.frame` to a data frame) containing the variables in the model. If not found in `data`, the variables are taken from `environment(formula)`, typically the environment from which `glm` is called.
`random`	an optional formula that specifies the random-effects structure or NULL.
`subset`	an optional vector specifying a subset of observations to be used in the fitting process.
`weights`	an optional vector of weights to be used in the fitting process. Should be `NULL` or a numeric vector.
`na.action`	a function which indicates what should happen when the data contain `NA`s. The default is set by the `na.action` setting of `options`, and is `na.fail` if that is unset. The ‘factory-fresh’ default is `na.omit`. Another possible value is `NULL`, no action. Value `na.exclude` can be useful.
`model`	a logical value indicating whether model frame should be included as a component of the returned value.
`x, y`	logical values indicating whether the response vector and model matrix used in the fitting process should be returned as components of the returned value.
`contrasts`	an optional list. See the `contrasts.arg` of `model.matrix.default`.
`method`	`NULL` or a character string, either "PQL" or "MQL", specifies the type of the quasilikelihood approximation to be used if a random-effects model is to be estimated.
`estimator`	a character string; either "ML" or "REML", specifies which estimator is to be used/approximated.
`dispersion`	a logical value or a character string; whether and how a dispersion parameter should be estimated. For details see `dispersion`.
`from.table`	a logical value; do the data represent a contingency table, e.g. were created by applying `as.data.frame()` a the result of `table()` or `xtabs()`. This relevant only if the response is a factor. This argument should be set to `TRUE` if the data do come from a contingency table. Correctly setting `from.table=TRUE` in this case, will lead to efficiency gains in computing, but more importantly overdispersion will correctly be computed if present.
`groups`	an optional formula that specifies groups of observations relevant for the specification of overdispersed response counts.
`control`	a list of parameters for the fitting process. See `mclogit.control`
`...`	arguments to be passed to `mclogit.control` or `mmclogit.control`

Details

The function mblogit internally rearranges the data into a 'long' format and uses mclogit.fit to compute estimates. Nevertheless, the 'user data' is unaffected.

Value

mblogit returns an object of class "mblogit", which has almost the same structure as an object of class "glm". The difference are the components coefficients, residuals, fitted.values, linear.predictors, and y, which are matrices with number of columns equal to the number of response categories minus one.

References

Agresti, Alan (2002). Categorical Data Analysis. 2nd ed, Hoboken, NJ: Wiley. doi: 10.1002/0471249688

Breslow, N.E. and D.G. Clayton (1993). "Approximate Inference in Generalized Linear Mixed Models". Journal of the American Statistical Association 88 (421): 9-25. doi: 10.1080/01621459.1993.10594284

Examples

library(MASS) # For 'housing' data
library(nnet)
library(memisc)

(house.mult<- multinom(Sat ~ Infl + Type + Cont, weights = Freq,
                       data = housing))


(house.mblogit <- mblogit(Sat ~ Infl + Type + Cont, weights = Freq,
                         data = housing))

summary(house.mult)

summary(house.mblogit)

mtable(house.mblogit)

mclogit

Multinomial Logit Models, with or without Random Effects or Overdispersion

v0.8.7.3

GPL-2

Authors

Martin Elff

Initial release

2021-03-19