Maximum Likelihood (ML) estimation of a logit model.
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 )A list.
numeric vector, the binary process
numeric matrix, the regressors
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
numeric vector, either of length 1 or the number of parameters to be estimated, see nlminb
numeric vector, either of length 1 or the number of parameters to be estimated, see nlminb
an integer that determines the expression for the coefficient-covariance, see "details"
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
a list passed on to the control argument of nlminb
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
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
Genaro Sucarrat, http://www.sucarrat.net/
No details for the moment.
No references for the moment.