Estimation of a logit model

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.

References

No references for the moment.

Author

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

See also

nlminb, solve

Examples

##no examples for the moment