Eva Poen <[email protected]>:
Does the outcome variable have only 21 categories {0,...20} or is it continuous?
Maybe you could produce 21 histograms (with fractions for each of the
models overlaid or "interlaced" on one graph) characterizing the
distribution of observed values for those predicted to have Y=0, ...
20. See -byhist- on SSC for making interlaced histograms.
On Thu, Jan 15, 2009 at 11:59 AM, Eva Poen <[email protected]> wrote:
> Dear all,
>
> currently I am working on slightly complicated mixture models for my
> data. My outcome variable is bounded between 0 and 20, and has mass at
> either end of the interval. Whether or not I analyse the data on the
> original [0,20] scale or a transformation to [0,1] (fractions) does
> not make any difference to me.
>
> My question concerns the goodness of fit. I would like to compare the
> goodness fit of the complicated finite mixture model to much simpler
> models, e.g. the tobit model, the glm model. and a hurdle
> specification. Since the likelihood values of these models differ
> substantially, likelihood based measures such as BIC appear to be
> inadequate for the purpose. Also, measures that compare the model
> likelihood of the fitted model to the null likelihood ("pseudo r2")
> are difficult sine I can calculate them for the tobit and glm models,
> but not for the mixture model, as it is unclear what the null model
> would be.
>
> So far I have been looking at crude measures like correlation between
> predicted outcome and actual outcome, but I feel that this is
> inadequate, especially since the outcome variable is bounded. I'd be
> grateful for hints and comments. I am working with Stata 9.2.
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