Date:         Tue, 18 Jun 2002 15:54:37 +0100
Reply-To:     david.mcnulty@QUESTINTL.COM
Sender:       "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From:         david.mcnulty@QUESTINTL.COM
Subject:      Untenable results from PROC GENMOD
Content-type: text/plain; charset=us-ascii

Hi Folks,

I am trying to estimate some proportions in a three way table using Proc
GENMOD and am getting some very curious results. In brief after back
transforming the estimated LSMeans, the proportions have standard errors
greater than 1.

Example:  LSmean Prod_1 = 2.1478, se = 1622.330
proportion = Exp(mu)/(1+Exp(mu))= 0.895
approx se = p(1-p).SE(LSMean) = 151.86

(Since n is 31, the se using the approximation sqrt(p(1-p)/n) is closer to
0.05)

Is there something I can tweak in Proc GENMOD to get some sensible answers?

Thanks

Dave.



Additional Info
================

In the following code the binomial response "correct" has the form 1 or 0
rather than the alternate events/trial syntax. Product "Prod_8" is excluded
from the analysis since the empirical proportion for Product="Prod_8" and
phase="P3" is one. Since log(1/(1-1)) is undefined SAS warns the Hessian is
not positive definite and refuses to calculate the remaining LSMeans.

Code
====
proc genmod data=master descending;
   class subject phase product;
   model correct=subject phase|product / type1 type3 dist=bin;
   lsmeans phase|product ;
   where product ne 'Prod_8';
quit;

Sample Output
=============
                                       The SAS System       10:57 Tuesday,
June 18, 2002 126

                                    The GENMOD Procedure

                             LR Statistics For Type 3 Analysis

                                                    Chi-
                      Source               DF     Square    Pr > ChiSq

                      Subject              30      58.02        0.0016
                      Phase                 1       0.17        0.6775
                      Product               6      26.30        0.0002
                      Phase*Product         6       2.97        0.8124


                                    Least Squares Means

                                                     Standard
Chi-
 Effect          Phase  Product           Estimate      Error     DF
Square   Pr > ChiSq

 Phase           P2                         1.9958   1622.330      1
0.00       0.9990
 Phase           P3                         2.1003   1622.330      1
0.00       0.9990
 Product                Prod_1              2.1478   1622.330      1
0.00       0.9989
 Product                Prod_2              2.7500   1622.330      1
0.00       0.9986
 Product                Prod_3              2.3726   1622.330      1
0.00       0.9988
 Product                Prod_4              1.5691   1622.330      1
0.00       0.9992
 Product                Prod_5              0.8329   1622.330      1
0.00       0.9996
 Product                Prod_6              2.4147   1622.330      1
0.00       0.9988
 Product                Prod_7              2.2489   1622.330      1
0.00       0.9989
 Phase*Product   P2     Prod_1              2.3594   1622.330      1
0.00       0.9988
 Phase*Product   P2     Prod_2              2.6068   1622.330      1
0.00       0.9987
 Phase*Product   P2     Prod_3              2.6068   1622.330      1
0.00       0.9987
 Phase*Product   P2     Prod_4              1.5691   1622.330      1
0.00       0.9992
 Phase*Product   P2     Prod_5              0.7539   1622.330      1
0.00       0.9996
 Phase*Product   P2     Prod_6              1.9362   1622.330      1
0.00       0.9990
 Phase*Product   P2     Prod_7              2.1384   1622.330      1
0.00       0.9989
 Phase*Product   P3     Prod_1              1.9362   1622.330      1
0.00       0.9990
 Phase*Product   P3     Prod_2              2.8933   1622.330      1
0.00       0.9986


ETC ...

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