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Re: st: pseudo R-squared
From
Steven Samuels <[email protected]>
To
[email protected]
Subject
Re: st: pseudo R-squared
Date
Mon, 4 Apr 2011 17:16:59 -0400
--
Gao, you must also make sure that all models are estimated on the same set of observations. That means you should exclude all observations with missing data for variables that appear in any of the models.
Steve
[email protected]
On Apr 3, 2011, at 6:48 PM, Richard Williams wrote:
"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."
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.
-------------------------------------------
Richard Williams, Notre Dame Dept of Sociology
OFFICE: (574)631-6668, (574)631-6463
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EMAIL: [email protected]
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