Example 10 for PROC CATMOD
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/* S A S S A M P L E L I B R A R Y */
/* */
/* NAME: CATEX10 */
/* TITLE: Example 10 for PROC CATMOD */
/* PRODUCT: STAT */
/* SYSTEM: ALL */
/* KEYS: categorical data analysis */
/* PROCS: CATMOD */
/* DATA: */
/* */
/* SUPPORT: Bob Derr */
/* REF: SAS/STAT User's Guide, PROC CATMOD chapter */
/* MISC: */
/* */
/****************************************************************/
/*----------------------------------------------------------------
Example 10: Direct Input of Response Functions and Covariance Matrix
Health Survey Data Analysis
---------------------------
Variational models are fit to health survey data. Estimates
of a well-being index have been computed for domains
corresponding to an age by sex cross-classification.
From: Koch and Stokes (1979).
----------------------------------------------------------------*/
data fbeing(type=est);
input b1-b5 _type_ $ _name_ $ b6-b10 #2;
datalines;
7.93726 7.92509 7.82815 7.73696 8.16791 parms .
7.24978 7.18991 7.35960 7.31937 7.55184
0.00739 0.00019 0.00146 -0.00082 0.00076 cov b1
0.00189 0.00118 0.00140 -0.00140 0.00039
0.00019 0.01172 0.00183 0.00029 0.00083 cov b2
-0.00123 -0.00629 -0.00088 -0.00232 0.00034
0.00146 0.00183 0.01050 -0.00173 0.00011 cov b3
0.00434 -0.00059 -0.00055 0.00023 -0.00013
-0.00082 0.00029 -0.00173 0.01335 0.00140 cov b4
0.00158 0.00212 0.00211 0.00066 0.00240
0.00076 0.00083 0.00011 0.00140 0.01430 cov b5
-0.00050 -0.00098 0.00239 -0.00010 0.00213
0.00189 -0.00123 0.00434 0.00158 -0.00050 cov b6
0.01110 0.00101 0.00177 -0.00018 -0.00082
0.00118 -0.00629 -0.00059 0.00212 -0.00098 cov b7
0.00101 0.02342 0.00144 0.00369 0.00253
0.00140 -0.00088 -0.00055 0.00211 0.00239 cov b8
0.00177 0.00144 0.01060 0.00157 0.00226
-0.00140 -0.00232 0.00023 0.00066 -0.00010 cov b9
-0.00018 0.00369 0.00157 0.02298 0.00918
0.00039 0.00034 -0.00013 0.00240 0.00213 cov b10
-0.00082 0.00253 0.00226 0.00918 0.01921
;
proc catmod data=fbeing;
title 'Complex Sample Survey Analysis';
response read b1-b10;
factors sex $ 2, age $ 5 / _response_=sex age
profile=(male '25-34',
male '35-44',
male '45-54',
male '55-64',
male '65-74',
female '25-34',
female '35-44',
female '45-54',
female '55-64',
female '65-74');
model _f_=_response_
/ design title='Main Effects for Sex and Age';
run;
contrast 'No Age Effect for Age<65' all_parms 0 0 1 0 0 -1,
all_parms 0 0 0 1 0 -1,
all_parms 0 0 0 0 1 -1;
run;
model _f_=(1 1 1,
1 1 1,
1 1 1,
1 1 1,
1 1 -1,
1 -1 1,
1 -1 1,
1 -1 1,
1 -1 1,
1 -1 -1)
(1='Intercept' ,
2='Sex' ,
3='Age (25-64 vs. 65-74)')
/ design title='Binary Age Effect (25-64 vs. 65-74)' ;
run;
quit;
