Multinomial Logit Models

Maximum Likelihood estimation of random utility discrete choice models (logit and probit).


Changes since version 0-1.0

* robust inference is provided with meat and estfunc methods
defined for mlogit models.

* subset argument is added to mlogit so that the model may be
estimated on a subset of alternatives.

* reflevel argument is added to mlogit which defines the base

* hmftest implements the Hausman McFadden test for the IIA

* function has been rewriten. It now use the reshape function.

* logitform class is provided to describe a logit model: update,
model.matrix and model.frame methods are available.

Changes since version 0.1-2

* major change, most of the package has been rewriten

* it is now possible to estimate heteroscedastic, nested and mixed
effects logit model

* the package doesn't depend any more on maxLik but a specific
optimization function is provided for efficiency reason

Changes since version 0.1-3

* mlogit didn't work when the dependent variable was an ordered
factor in a "wide-shaped" data.frame.

* the reflevel argument didn't work any more in version 0.1-3.

Changes since version 0.1-4

* if the choice variable is not an ordered factor, use as.factor()
instead of class() <- "factor"

* cov.mlogit, cor.mlogit, rpar , med, rg, stdev, mean functions
are added to extract and analyse random coefficients.

* a panel argument is added to mlogit so that mixed models with
repeated observation can be estimated using panel methods or not

* a problem with the weights argument is fixed

* the estimation of nested logit models with a unique elasticity
is now possible using un.nest.el = TRUE

* the estimation of nested logit models can now be done with or
without normalization depending on the value of the argument

Changes since version 0.1-5

* a third part of the formula is added : it concerns alternative
specific variables with alternative specific coefficients

* improved presentation for the Fishing dataset.

* a bug (forgotten drop = FALSE) corrected in

* Electricity and ModeCanada datasets are added

Changes since version 0.1-6

* a bug in mFormula (effects vs variable) is fixed

Changes since version 0.1-7

* mFormula modified so that models can be updated

* likelihood has been rewriten for the heteroscedastic logit
model, the computation is now much faster

* nested logit models with overlapping nests are now supported;
nests = "pcl" enables the estimation of the pair combinatorial
logit model

* the norm argument is added to rpar

* the logLik argument is now of class logLik

* is modified so that an id argument can be used with
data in long shape

* the argument of used to define longitudinal data is
now called id.var

* mlogit.lnls is corrected so that the estimation of multinomial
models can handle unbalanced data (pb with Reduce)

* the three tests are temporary removed

Changes since version 0.1-8

* all the models could normally be estimated on unbalanced data

* the three tests are added, i.e. a new scoretest function and
specific methods for waldtest and lrtest from the lmtest package

* the model.matrix method for mlogit objects is now exported

Changes since version 0.2-0

* all the rda files are now compressed

Changes since version 0.2-1

* ranked-order models can be now estimated ; a new argument called
'ranked' is introduced in which performs the relevant
transformation of the data.frame. The estimated model is then a
standard multinomial logit model

* multinomial probit model is now estimated by setting the new
probit arguments to TRUE

* for the mixed logit model, different draws are now used for each

* a predict method is now available for mlogit objects

* a coef method is added which removes the fixed argument

* constPar can now be a named numeric vector. In this case,
default starting values are changed according to constPar

* the vcov method for mlogit objects is greatly enhanced.

* mlogit objects now have two elements which indicate the fitted
probabilities : fitted is the estimated probability for the
outcome and probabilities contains the fitted probabilities for
all the alternatives

* mentions to 'alt' in the names of the effects is canceled ;
moreover, the intercepts are now called altname:('intercept')

* a 'choice' attribute is added to objects

* an effects method is provided, which computes the marginal
effects of a covariate

Changes since version 0.2-2

* some sys.frame() changed to parent.frame() 

Changes since version 0.2-3

* the list of primes used to generate halton sequences was too
short, its length has been increased

* halton sequences where used to estimate mixed logit even for
the default value of halton (NULL), this has been fixed

* the contribution of each observation to the gradient is not
returned as the 'gradient' element of mlogit objects

* the distributions are now checked for rpar and an error is
returned in case of unknown distribution

Changes since version 0.2-4

* the id series (one observation per choice situation) was
      badly constructed, it is now fixed

* the levels of the choice variable are now equalized to the
      those of the alt variable, allowing the case were some
      alternatives are never chosen

* mlogit is now able to estimate models with singular matrix
      of covariates. At the end of model.matrix.mformula, the
      linear dependent columns of X are removed

* group-hetheroscedastic model can be estimated by setting the
  relevant covariates in the 4th part of the formula

* correlation can still be a boolean, but can also be a
  character vector if one wants that a subset of the random
  parameters being correlated

    * zbu and zbt distributions are added : these are
      one-parameter distributions for which the lower bond is 0.

* there was a bug in the triangular distribution which is now

* bug in the effects method fixed

* a new iv function is provided
* the linear predictor is now returned

Reference manual

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