Thank you for your response. I am working with multinomial logit models and
using survey data. I would like to see if adding interactions to my model
improves the fit. Given that I'm working with survey data, what is the best
way to compare model fit?
On Thu, Feb 11, 2010 at 10:21 AM, Martin Weiss <[email protected]> wrote:
>
> <>
>
>
> You may want to consult [SVY], p. 76:
>
> "Most standard commands are also fit using maximum likelihood. When used
> with independently
> distributed, nonweighted data, the likelihood to be maximized reflects the
> joint probability distribution
> of the data given the chosen model. With complex survey data, however, this
> interpretation of the
> likelihood is no longer valid, because survey data are weighted, not
> independently distributed, or
> both. Yet for survey data, (valid) parameter estimates for a given model
> can
> be obtained using the
> associated likelihood function with appropriate weighting. Because the
> probabilistic interpretation no
> longer holds, the likelihood here is instead called a pseudolikelihood, but
> likelihood-ratio tests are no
> longer valid."
>
>
>
> HTH
> Martin
>
>
> -----Ursprüngliche Nachricht-----
> Von: [email protected]
> [mailto:[email protected]] Im Auftrag von Kate Perper
> Gesendet: Donnerstag, 11. Februar 2010 15:54
> An: [email protected]
> Betreff: st: fit statistics for svy: mlogit
>
> Hello,
>
> I'm wondering how I can get fit statistics when running an multinomial
> logistic regression model while controlling for survey design. svy: mlogit
>
> When I run mlogit without svy, I can get log likelihood statistics, not so
> when svy is included.
>
> Thanks,
>
> Kate
>
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