mblogit  R Documentation 
The function mblogit
fits baselinecategory logit models for categorical
and multinomial count responses with fixed alternatives.
mblogit( formula, data = parent.frame(), random = NULL, catCov = c("free", "diagonal", "single"), subset, weights = NULL, na.action = getOption("na.action"), model = TRUE, x = FALSE, y = TRUE, contrasts = NULL, method = NULL, estimator = c("ML", "REML"), dispersion = FALSE, start = NULL, from.table = FALSE, groups = NULL, control = if (length(random)) mmclogit.control(...) else mclogit.control(...), ... )
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 
random 
an optional formula or list of formulas that specify the randomeffects structure or NULL. 
catCov 
a character string that specifies optional restrictions on the covariances of random effects between the logit equations. "free" means no restrictions, "diagonal" means that random effects pertinent to different categories are uncorrelated, while "single" means that the random effect variances pertinent to all categories are identical. 
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 
na.action 
a function which indicates what should happen when the data
contain 
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 
method 

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

start 
an optional matrix of starting values (with as many rows
as logit equations). If the model has random effects, the matrix
should have a "VarCov" attribute wtih starting values for
the random effects (co)variances. If the random effects model
is estimated with the "PQL" method, the starting values matrix
should also have a "random.effects" attribute, which should have
the same structure as the "random.effects" component of an object
returned by 
from.table 
a logical value; do the data represent a contingency table,
e.g. were created by applying 
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

... 
arguments to be passed to 
The function mblogit
internally rearranges the data into a
'long' format and uses mclogit.fit
to compute
estimates. Nevertheless, the 'user data' are unaffected.
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.
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): 925. doi: 10.1080/01621459.1993.10594284
The function multinom
in package nnet also
fits multinomial baselinecategory logit models, but has a slightly less
convenient output and does not support overdispersion or random
effects. However, it provides some other options. Baselinecategory logit
models are also supported by the package VGAM, as well as some
reducedrank and (semiparametric) additive generalisations. The package
mnlogit estimates logit models in a way optimized for large numbers
of alternatives.
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)
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