logit: Estimation of a logit model

In gets: General-to-Specific (GETS) Modelling and Indicator Saturation Methods

Estimation of a logit model

Description

Maximum Likelihood (ML) estimation of a logit model.

Usage

logit(y, x, initial.values = NULL, lower = -Inf, upper = Inf, 
    method = 2, lag.length = NULL, control = list(), eps.tol = .Machine$double.eps, 
    solve.tol = .Machine$double.eps )

Arguments

y	numeric vector, the binary process
x	numeric matrix, the regressors
initial.values	NULL or a numeric vector with the initial parameter values passed on to the optimisation routine, nlminb. If NULL, the default, then the values are chosen automatically
lower	numeric vector, either of length 1 or the number of parameters to be estimated, see nlminb
upper	numeric vector, either of length 1 or the number of parameters to be estimated, see nlminb
method	an integer that determines the expression for the coefficient-covariance, see "details"
lag.length	NULL or an integer that determines the lag-length used in the robust coefficient covariance. If lag.length is an integer, then it is ignored unless method = 3
control	a list passed on to the control argument of nlminb
eps.tol	numeric, a small value that ensures the fitted zero-probabilities are not too small when the log-transformation is applied when computing the log-likelihood
solve.tol	numeric value passed on to the tol argument of solve, which is called whenever the coefficient-coariance matrix is computed. The value controls the toleranse for detecting linear dependence between columns when inverting a matrix

Details

No details for the moment.

Value

A list.

Author(s)

Genaro Sucarrat, http://www.sucarrat.net/

References

No references for the moment.

See Also

nlminb, solve

Examples

##no examples for the moment

gets documentation built on Sept. 11, 2024, 9:03 p.m.