It seems to me much more sensical to use Stata's -glm- command when modeling
grouped binomial data; e.g. grouped logistic regression. You can model:
glm y x1 x2, fam(bin denom) eform
to immediately obtain grouped logistic results parameterized as odds ratios.
-demon- is the binomial denominator. You also get AIC, BIC, and other fit
statsitics on the screen, plus you can request a number of different types of
robust variance estimators. You can also bootstrap or jacknife standard errors
-- and use -sw- for stepwise methods. Moreover, you can then reuqest a
number of residuals including the standards ones and Anscombe, likelihood
residuals, etc. The Chi2 and deviance residuals come in raw, standardized or
studentized forms. Few of these statistics are available with glogit.
Joe Hilbe
==================================
Ethan Corona wrote:
As part of an epidemiology class project, I have to employ stepwise for
model selection. I can do that with no problems. My problem lies with the
lack of output that -stepwise- generates compared to the output that SAS
provides. Short of performing stepwise by hand, are there any model building
ADOs that give more detailed output of each intermediate model?
Specifically, I'm looking for type I and type III sum of squares as well as
regression output for each intermediate model.
I looked at -allpossible-, and while it provides all of the intermediate
models using the -details- information, it doesn't provide the sum of
squares that I'm looking for.
. . .
Hello again,
I realized that I failed to mention that I am doing logistic regression, so
-reg_ss- won't help me, either.
------------------------------------------------------------------------------
--
What do Type I and Type III sum of squares look like for logistic regression
in
SAS?
Is SAS using weighted least squares estimation on grouped data, as in
-glogit-?
Nick Cox mentioned -allpossible-; you should be able to do something with
-allpossible glogit- after construction of the indicator variables to get
the
contrasts that you're looking for. The technique for constructing the
difference contrast indicator variable to get -regress- to match -anova ,
partial- (Type III sums of squares) is shown in Sophia Rabe-Hesketh and
Brian
Everitt, _A Handbook of Statistical Analyses using Stata_ Second Edition.
(Boca
Raton: Chapman & Hall/CRC, 2000), pp. 73-75. The use of the technique with
-glogit- will be directly analogous to that with -regress-. You can also
use
-logit- with the same indicator variables to get an analogous contrast to
that
in -anova, partial-. I think that the SAS manuals variously call this a
"Type 3 test" or "Type III test," or something, outside the contexts of PROC
ANOVA and PROC GLM.
Joseph Coveney
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