logit {gets} | R Documentation |
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 )
y |
numeric vector, the binary process |
x |
numeric matrix, the regressors |
initial.values |
|
lower |
numeric vector, either of length 1 or the number of parameters to be estimated, see |
upper |
numeric vector, either of length 1 or the number of parameters to be estimated, see |
method |
an integer that determines the expression for the coefficient-covariance, see "details" |
lag.length |
|
control |
a |
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 |
No details for the moment.
A list
.
Genaro Sucarrat, http://www.sucarrat.net/
No references for the moment.
##no examples for the moment