Re: st: pseudo R-squared

[Richard Williams <richardwilliams.ndu@gmail.com>]

At 04:15 PM 4/3/2011, Gao Liu wrote:
> Thanks, Nick. Right, it should be gologit2. So it is McFadden's Pseudo R^2.
>
> Can I simply use the difference of the pseudo's R^2 of model with the
> focal variable and that without to explain the increased explanatory
> power? pseudo-R2 (with the variable)-pseudo-R2 (without the
> variable)=(L1(with)-L1(without))/L0. Can I use this result as the
> increased explanatory power? Are there any other ways to  answer the
> question of "how much more likely is the predictor able to predict the
> change of categorical dependent variable"
>
> Thanks very much for your explanation.
First off, thanks to Nick for answering the first part of the Q. 
gologit2 uses the McFadden's Pseudo R^2, just like logit and ologit 
and assorted other Stata routines do. If somebody had a burning 
desire to calculate one of the other Pseudo R^2 measures it probably 
would not be too hard to do so.
With regards to the 2nd part of the question, I think the most 
customary thing to do is either a Wald test of the variable in 
question or a LR chi-square contrast between the model that has the 
variable and the model that doesn't. So, if the variable of interest 
was education, you could do something like
use "http://www.indiana.edu/~jslsoc/stata/spex_data/ordwarm2.dta";, clear
gologit2  warm yr89 male white age prst ed
test ed
nestreg, lr: gologit2 warm (yr89 male white age prst) (ed)

When contrasting models, just make sure they really are nested, e.g. 
you don't want to be imposing different proportionality constraints 
in your different models while at the same time dropping or adding variables.

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Richard Williams, Notre Dame Dept of Sociology
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