mclogit 0.4 published on CRAN¶
One highlight of the new release is improved support for multinomial
baseline logit models, i.e. models for categorical dependent variables
with unordered categories (a.k.a. nominal-level dependent variables)
with help of the function
mblogit(). Such models could already be
mclogit(), yet this required some additional work in
data-management and model construction.
mblogit() function allows to fit models with categorical
dependent variables right away. Its output will be numerically identical
with the output of
multinom from the package
nnet, but the results are
reported in more “conventional” way similar to to the results of
glm() and the like.
At present, there is no support for random effects in multinomial baseline logit models. These are going to be added in the next release (0.5), along with random slopes in multinomial conditional logit models.
Changes since 0.3¶
- New function
mblogitto fit multinomial baseline logit models.
mclogitobjects, so that
drop1.defaultshould work with these.
mclogit.fitnow allow user-provided starting values.
getSummarymethods now return “contrasts” and “xlevels” components.
- Fixed prediction method for
- Corrected handling of weights and standard errors of prediction.
- Matrices returned by the
vcov()have row and column names.
mclogit.fit.rePQLare exported to enable their use by other packages.