I'm sure below is fine but john fox's CAR book has some nice examples
of how to compute the logit parameters and variances from scratch using
iteratively weighted least squares.
On Thu, Jan 29, 2009 at 1:54 AM, justin bem wrote:
Run
outfit<-nlm(..., hessian=T) and then standards error are
se<-diag(solve(outfit$hessian))
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Justin BEM
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________________________________
De : Bomee Park <bom...@stanford.edu>
�� : r-help@r-project.org
Envoy�� le : Jeudi, 29 Janvier 2009, 4h01mn 56s
Objet��: [R] standard error of logit parameters
Hi everyone.
I am now estimating the parameters for a logit model, and trying to
get the estimates by laximizing the log_likelihood.
The nlm function works nicely for maximizing the -(log_likelihood) and
returns the parameter estimates that minimize the static, and the
gradients also, but don't have any clue how I can get the standard
error for the parameters.
Any help will be greatly appreciated.
Thanks.
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______________________________________________
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PLEASE do read the posting guide
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and provide commented, minimal, self-contained, reproducible code.
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
