estimate.mlogit {mlogitBMA}

Multinomial Logit Estimation

Description

Maximum likelihood estimation of coefficients of one or more multinomial logit models.

Usage

## S3 method for class 'formula'
 estimate.mlogit(f, data, method = "BHHH", 
                 choices = NULL, base.choice = 1, 
                 varying = NULL, sep = ".", ...)
	
## S3 method for class 'mnl.spec'
 estimate.mlogit(object, data, method='BHHH', ...)

## S3 method for class 'bic.mlogit'
 estimate.mlogit(object, ...)

## S3 method for class 'list'
 estimate.mlogit(object, data, verbose=TRUE, ...)

Arguments

f

Formula as described in Details of mnl.spec.

object

An object of class mnl.spec containing the model specification, or an object of class bic.mlogit, or a list of objects of class mnl.spec.

data

Data frame containing the variables of the model.

method

Estimation method passed to the maxLik function of the maxLik package. Available methods are “Newton-Raphson”, “BFGS”, “BHHH”, “SANN” or “NM”.

choices

Vector of names of alternatives. If it is not given, it is determined from the response column of the data frame. Values of this vector should match or be a subset of those in the response column. If it is a subset, data is reduced to contain only observations whose choice is contained in choices.

base.choice

Index of the base alternative within the vector choices.

varying

Indices of variables within data that are alternative-specific.

sep

Separator of variable name and alternative name in the ‘varying’ variables.

verbose

Logical switching log messages on and off.

...

Arguments passed to the underlying optimization routine in optim. Note that arguments data and method can be also passed to estimate.mlogit.bic.mlogit and estimate.mlogit.list.

Details

The data are expected to be in the ‘wide’ format (using the terminology of the reshape function). There should be one record for each individual. Alternative-specific variables occupy single column per alternative. The given optimization routine is called for the multinomial data, starting from the coefficients being all zeros.

Function estimate.mlogit.bic.mlogit invokes as many estimations as there are models selected in the bic.mlogit object. Function estimate.mlogit.list invokes an estimation for each specification included in the object argument.

Value

Functions estimate.mlogit.formula and estimate.mlogit.mnl.spec return an object of class mnl. Functions estimate.mlogit.bic.mlogit and estimate.mlogit.list return a list of such objects with each element corresponding to one specification. An object of class mnl contains the following components:

coefficients

The estimated coefficients.

logLik

Maximum log-likelihood.

logLik0

Null log-likelihood.

aic

Akaike Information Criterium.

bic

Bayesian Information Criterium.

iter

Number of iterations.

hessian

The Hessian at the maximum.

gradient

The last gradient value.

fitted.values

The MNL probabilities computed with the estimated parameters.

residuals

Difference between observed values and fitted values.

specification

The corresponding mnl.spec object.

convergence

Convergence statistics.

method

Estimation method.

time

Time needed for the estimation.

code

Code returned by the maxLik function.

message

Message describing the code.

last.step

List describing the last unsuccessful step if code=3 (see maxLik).

Author(s)

Hana Sevcikova

References

Train, K.E. (2003) Discrete Choice Methods with Simulation. Cambridge University Press.

See Also

summary.mnl, mnl.spec, reshape, maxLik

Examples

data(heating)
est <- estimate.mlogit(depvar ~ ic + oc, heating, choices=1:5, 
                       varying=c(3:12, 20:24), sep='')
summary(est)