Multinomial Logit Models

Maximum Likelihood estimation of random utility discrete choice models, as described in Kenneth Train (2012) Discrete Choice Methods with Simulations .


mlogit 0.4-1

  • the Cracker, Catsup and Car data set are back in mlogit since AER, flexmix and mlogitBMA run examples based on them.

  • the alt vector in the index is now carrefully checked in case of alternative subseting or reference level change.

mlogit 0.4-0

  • the main vignette is improved, writen in markdown and now and split by sections

  • the Exercises vignette is splited and is now writen in markdown

  • importantly, the Cholesky matrix is now coerced to a vector by rows and note by columns.

  • the mlogit function was checked and improved.

  • implementation of the computation of the standard deviations of the covariance matrix of the random parameters, using the delta method.

  • some data sets are removed

mlogit 0.3-0

New features

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

  • a logsum function is provided to compute the log-sum or the inclusive utility of a random utility model,

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

  • the linear predictor is now returned by mlogit,

  • 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.

data sets

  • the RiskyTransport data set (used in the vignette to illustrate the estimation of the mixed logit model

  • the NOx data set (used in the vignette to illustrate the estimation of the multinomial and group-heteroscedastic logit model),

  • the JapaneseFDI data set (used in the vignette to illustrate the estimation of the nested logit model)


A new vignette called mlogit2 is added ; this is the draft version of an article submitted to the Journal of Statistical Software ; it is less exhaustive, but better writen thant the original mlogit vignette.


  • 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

  • there was a bug in the triangular distribution which is now fixed

  • bug in the effects method fixed

mlogit 0.2-4

  • 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

mlogit 0.2-3

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

mlogit 0.2-2

  • 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 observation

  • 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

mlogit 0.2-1

  • all the rda files are now compressed

mlogit 0.2-0

  • 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

mlogit 0.1-8

  • 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

mlogit 0.1-7

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

mlogit 0.1-6

  • 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 model.matrix.mFormula

  • Electricity and ModeCanada datasets are added

mlogit 0.1-5

  • 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 unscaled

mlogit 0.1-4

  • 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.

mlogit 0.1-3

  • 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

mlogit 0.1-2

  • 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 alternative.

  • hmftest implements the Hausman McFadden test for the IIA hypothesis.

  • 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.

mlogit 0.1-1

mlogit 0.1-0

Reference manual

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