Multinomial (Baseline) Logit Models for Categorical and Multinommial Responses¶
Description¶
The function mblogit
fits multinomial logit models for categorical and multinomial count responses with fixed alternatives, where the logits are relative to a baseline category.
Usage¶
mblogit(formula, data = parent.frame(), random = NULL, subset,
weights = NULL, na.action = getOption("na.action"), model = TRUE,
x = FALSE, y = TRUE, contrasts = NULL, control = 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 indata
, the variables are taken fromenvironment(formula)
, typically the environment from whichglm
is called. random

an optional formula that specifies the randomeffects 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 ofoptions
, and isna.fail
if that is unset. The ‘factoryfresh’ default isna.omit
. Another possible value isNULL
, no action. Valuena.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
ofmodel.matrix.default
. control

a list of parameters for the fitting process. See
mclogit.control
...

arguments to be passed to
mclogit.control
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.
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.