Multinomial (Baseline) Logit Models for Categorical and Multinommial Responses¶
mblogit fits multinomial logit models for categorical and multinomial
count responses with fixed alternatives, where the logits are relative to a baseline
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(...), ...)
the model formula. The response must be a factor or a matrix of counts.
an optional data frame, list or environment (or object coercible by
as.data.frameto 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
an optional formula that specifies the random-effects structure or NULL.
an optional vector specifying a subset of observations to be used in the fitting process.
an optional vector of weights to be used in the fitting process. Should be
NULLor a numeric vector.
a function which indicates what should happen when the data contain
NA``s. The default is set by the ``na.actionsetting of
options, and is
na.failif that is unset. The ‘factory-fresh’ default is
na.omit. Another possible value is
NULL, no action. Value
na.excludecan be useful.
a logical value indicating whether model frame should be included as a component of the returned value.
logical values indicating whether the response vector and model matrix used in the fitting process should be returned as components of the returned value.
an optional list. See the
a list of parameters for the fitting process. See
arguments to be passed to
mblogit returns an object of class “mblogit”, which has almost the same structure as
an object of class “glm”. The difference are the components
y, which are matrices
with number of columns equal to the number of response categories minus one.
mblogit internally rearranges the data into a ‘long’ format and uses
mclogit.fit to compute estimates. Nevertheless, the ‘user data’ is unaffected.