estimate.mlogit: Multinomial Logit Estimation

In mlogitBMA: Bayesian Model Averaging for Multinomial Logit Models

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)

mlogitBMA documentation built on April 14, 2022, 1:07 a.m.