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Re: st: pseudo R-squared
From
Richard Williams <[email protected]>
To
[email protected]
Subject
Re: st: pseudo R-squared
Date
Sun, 03 Apr 2011 18:48:59 -0400
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
HOME: (574)289-5227
EMAIL: [email protected]
WWW: http://www.nd.edu/~rwilliam
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