SAS for Chi-Squared and Measures of Association with Table 3.2
-----------------------------------------------------------------------------
data table;
input degree religion $ count ;
datalines;
1 fund 178
1 mod 138
1 lib 108
2 fund 570
2 mod 648
2 lib 442
3 fund 138
3 mod 252
3 lib 252
;
proc freq order=data; weight count;
tables degree*religion / chisq expected measures cmh1;
proc genmod order=data; class degree religion;
model count = degree religion / dist=poi link=log residuals;
-----------------------------------------------------------------------------
SAS for Fisher's Exact Test and Confidence Intervals for Odds Ratio for Table 3.8
-----------------------------------------------------------------------------
data fisher;
input poured guess count ;
datalines;
1 1 3
1 2 1
2 1 1
2 2 3
;
proc freq; weight count;
tables poured*guess / measures riskdiff;
exact fisher or / alpha=.05;
proc logistic descending; freq count;
model guess = poured / clodds=pl;
-----------------------------------------------------------------------------
SAS for Binary GLMs for Snoring Data in Table 4.2
------------------------------------------------------------------------- data glm; input snoring disease total ; datalines; 0 24 1379 2 35 638 4 21 213 5 30 254 ; proc genmod; model disease/total = snoring / dist=bin link=identity; proc genmod; model disease/total = snoring / dist=bin link=logit; proc genmod; model disease/total = snoring / dist=bin link=probit; -------------------------------------------------------------------------
SAS for Poisson and Negative Binomial GLMs for Crab Data of Table 4.3
-------------------------------------------------------------- data crab; input color spine width satell weight; weight=weight/1000; color=color-1; datalines; 3 3 28.3 8 3050 4 3 22.5 0 1550 2 1 26.0 9 2300 4 3 24.8 0 2100 4 3 26.0 4 2600 3 3 23.8 0 2100 2 1 26.5 0 2350 4 2 24.7 0 1900 3 1 23.7 0 1950 4 3 25.6 0 2150 4 3 24.3 0 2150 3 3 25.8 0 2650 3 3 28.2 11 3050 5 2 21.0 0 1850 3 1 26.0 14 2300 2 1 27.1 8 2950 3 3 25.2 1 2000 3 3 29.0 1 3000 5 3 24.7 0 2200 3 3 27.4 5 2700 3 2 23.2 4 1950 2 2 25.0 3 2300 3 1 22.5 1 1600 4 3 26.7 2 2600 5 3 25.8 3 2000 5 3 26.2 0 1300 3 3 28.7 3 3150 3 1 26.8 5 2700 5 3 27.5 0 2600 3 3 24.9 0 2100 2 1 29.3 4 3200 2 3 25.8 0 2600 3 2 25.7 0 2000 3 1 25.7 8 2000 3 1 26.7 5 2700 5 3 23.7 0 1850 3 3 26.8 0 2650 3 3 27.5 6 3150 5 3 23.4 0 1900 3 3 27.9 6 2800 4 3 27.5 3 3100 2 1 26.1 5 2800 2 1 27.7 6 2500 3 1 30.0 5 3300 4 1 28.5 9 3250 4 3 28.9 4 2800 3 3 28.2 6 2600 3 3 25.0 4 2100 3 3 28.5 3 3000 3 1 30.3 3 3600 5 3 24.7 5 2100 3 3 27.7 5 2900 2 1 27.4 6 2700 3 3 22.9 4 1600 3 1 25.7 5 2000 3 3 28.3 15 3000 3 3 27.2 3 2700 4 3 26.2 3 2300 3 1 27.8 0 2750 5 3 25.5 0 2250 4 3 27.1 0 2550 4 3 24.5 5 2050 4 1 27.0 3 2450 3 3 26.0 5 2150 3 3 28.0 1 2800 3 3 30.0 8 3050 3 3 29.0 10 3200 3 3 26.2 0 2400 3 1 26.5 0 1300 3 3 26.2 3 2400 4 3 25.6 7 2800 4 3 23.0 1 1650 4 3 23.0 0 1800 3 3 25.4 6 2250 4 3 24.2 0 1900 3 2 22.9 0 1600 4 2 26.0 3 2200 3 3 25.4 4 2250 4 3 25.7 0 1200 3 3 25.1 5 2100 4 2 24.5 0 2250 5 3 27.5 0 2900 4 3 23.1 0 1650 4 1 25.9 4 2550 3 3 25.8 0 2300 5 3 27.0 3 2250 3 3 28.5 0 3050 5 1 25.5 0 2750 5 3 23.5 0 1900 3 2 24.0 0 1700 3 1 29.7 5 3850 3 1 26.8 0 2550 5 3 26.7 0 2450 3 1 28.7 0 3200 4 3 23.1 0 1550 3 1 29.0 1 2800 4 3 25.5 0 2250 4 3 26.5 1 1967 4 3 24.5 1 2200 4 3 28.5 1 3000 3 3 28.2 1 2867 3 3 24.5 1 1600 3 3 27.5 1 2550 3 2 24.7 4 2550 3 1 25.2 1 2000 4 3 27.3 1 2900 3 3 26.3 1 2400 3 3 29.0 1 3100 3 3 25.3 2 1900 3 3 26.5 4 2300 3 3 27.8 3 3250 3 3 27.0 6 2500 4 3 25.7 0 2100 3 3 25.0 2 2100 3 3 31.9 2 3325 5 3 23.7 0 1800 5 3 29.3 12 3225 4 3 22.0 0 1400 3 3 25.0 5 2400 4 3 27.0 6 2500 4 3 23.8 6 1800 2 1 30.2 2 3275 4 3 26.2 0 2225 3 3 24.2 2 1650 3 3 27.4 3 2900 3 2 25.4 0 2300 4 3 28.4 3 3200 5 3 22.5 4 1475 3 3 26.2 2 2025 3 1 24.9 6 2300 2 2 24.5 6 1950 3 3 25.1 0 1800 3 1 28.0 4 2900 5 3 25.8 10 2250 3 3 27.9 7 3050 3 3 24.9 0 2200 3 1 28.4 5 3100 4 3 27.2 5 2400 3 2 25.0 6 2250 3 3 27.5 6 2625 3 1 33.5 7 5200 3 3 30.5 3 3325 4 3 29.0 3 2925 3 1 24.3 0 2000 3 3 25.8 0 2400 5 3 25.0 8 2100 3 1 31.7 4 3725 3 3 29.5 4 3025 4 3 24.0 10 1900 3 3 30.0 9 3000 3 3 27.6 4 2850 3 3 26.2 0 2300 3 1 23.1 0 2000 3 1 22.9 0 1600 5 3 24.5 0 1900 3 3 24.7 4 1950 3 3 28.3 0 3200 3 3 23.9 2 1850 4 3 23.8 0 1800 4 2 29.8 4 3500 3 3 26.5 4 2350 3 3 26.0 3 2275 3 3 28.2 8 3050 5 3 25.7 0 2150 3 3 26.5 7 2750 3 3 25.8 0 2200 4 3 24.1 0 1800 4 3 26.2 2 2175 4 3 26.1 3 2750 4 3 29.0 4 3275 2 1 28.0 0 2625 5 3 27.0 0 2625 3 2 24.5 0 2000 ; proc genmod; model satell = width / dist=poi link=log ; proc genmod; model satell = width / dist=poi link=identity ; proc genmod; model satell = width / dist=negbin link=identity ; ---------------------------------------------------------------
SAS for Overdispersion Modeling of Teratology Data in Table 4.5
------------------------------------------------------------------------ data moore; input litter group n y ; datalines; 1 1 10 1 2 1 11 4 3 1 12 9 4 1 4 4 5 1 10 10 6 1 11 9 7 1 9 9 8 1 11 11 9 1 10 10 10 1 10 7 11 1 12 12 12 1 10 9 13 1 8 8 14 1 11 9 15 1 6 4 16 1 9 7 17 1 14 14 18 1 12 7 19 1 11 9 20 1 13 8 21 1 14 5 22 1 10 10 23 1 12 10 24 1 13 8 25 1 10 10 26 1 14 3 27 1 13 13 28 1 4 3 29 1 8 8 30 1 13 5 31 1 12 12 32 2 10 1 33 2 3 1 34 2 13 1 35 2 12 0 36 2 14 4 37 2 9 2 38 2 13 2 39 2 16 1 40 2 11 0 41 2 4 0 42 2 1 0 43 2 12 0 44 3 8 0 45 3 11 1 46 3 14 0 47 3 14 1 48 3 11 0 49 4 3 0 50 4 13 0 51 4 9 2 52 4 17 2 53 4 15 0 54 4 2 0 55 4 14 1 56 4 8 0 57 4 6 0 58 4 17 0 ; proc genmod; class group; model y/n = group / dist=bin link=identity noint; estimate 'pi1-pi2' group 1 -1 0 0; proc genmod; class group; model y/n = group / dist=bin link=identity noint scale=pearson; ------------------------------------------------------------------------
SAS for Modeling Grouped Crab Data of Table 5.2
--------------------------------------------------------------------- data crab; input width y n satell; logcases=log(n); datalines; 22.69 5 14 14 23.84 4 14 20 24.77 17 28 67 25.84 21 39 105 26.79 15 22 63 27.74 20 24 93 28.67 15 18 71 30.41 14 14 72 ; proc genmod; model y/n = width / dist=bin link=logit lrci alpha=.01 type3; proc logistic; model y/n = width / influence stb; output out=predict p=pi_hat lower=LCL upper=UCL; proc print data=predict; proc genmod; model satell = width / dist=poi link=log offset=logcases residuals; ---------------------------------------------------------------------
SAS for Logit Modeling of AIDS Data in Table 5.5
----------------------------------------------------------------------- data aids; input race $ azt $ y n ; datalines; White Yes 14 107 White No 32 113 Black Yes 11 63 Black No 12 55 ; proc genmod; class race azt; model y/n = azt race / dist=bin type3 lrci residuals obstats; proc logistic; class race azt / param=reference; model y/n = azt race / aggregate scale=none clparm=both clodds=both; output out=predict p=pi_hat lower=lower upper=upper; proc print data=predict; proc logistic; class race azt (ref=first) / param=ref; model y/n = azt / aggregate=(azt race) scale=none; -----------------------------------------------------------------------
SAS for Logistic Regression Models with Crab Data of Table 4.3
---------------------------------------------------------------------------------- data crab; input color spine width satell weight; weight=weight/1000; color=color-1; if satell>0 then y=1; if satell=0 then y=0; if color=4 then light=0; if color < 4 then light=1; datalines; 3 3 28.3 8 3050 4 3 22.5 0 1550 2 1 26.0 9 2300 4 3 24.8 0 2100 4 3 26.0 4 2600 3 3 23.8 0 2100 2 1 26.5 0 2350 4 2 24.7 0 1900 3 1 23.7 0 1950 4 3 25.6 0 2150 4 3 24.3 0 2150 3 3 25.8 0 2650 3 3 28.2 11 3050 5 2 21.0 0 1850 3 1 26.0 14 2300 2 1 27.1 8 2950 3 3 25.2 1 2000 3 3 29.0 1 3000 5 3 24.7 0 2200 3 3 27.4 5 2700 3 2 23.2 4 1950 2 2 25.0 3 2300 3 1 22.5 1 1600 4 3 26.7 2 2600 5 3 25.8 3 2000 5 3 26.2 0 1300 3 3 28.7 3 3150 3 1 26.8 5 2700 5 3 27.5 0 2600 3 3 24.9 0 2100 2 1 29.3 4 3200 2 3 25.8 0 2600 3 2 25.7 0 2000 3 1 25.7 8 2000 3 1 26.7 5 2700 5 3 23.7 0 1850 3 3 26.8 0 2650 3 3 27.5 6 3150 5 3 23.4 0 1900 3 3 27.9 6 2800 4 3 27.5 3 3100 2 1 26.1 5 2800 2 1 27.7 6 2500 3 1 30.0 5 3300 4 1 28.5 9 3250 4 3 28.9 4 2800 3 3 28.2 6 2600 3 3 25.0 4 2100 3 3 28.5 3 3000 3 1 30.3 3 3600 5 3 24.7 5 2100 3 3 27.7 5 2900 2 1 27.4 6 2700 3 3 22.9 4 1600 3 1 25.7 5 2000 3 3 28.3 15 3000 3 3 27.2 3 2700 4 3 26.2 3 2300 3 1 27.8 0 2750 5 3 25.5 0 2250 4 3 27.1 0 2550 4 3 24.5 5 2050 4 1 27.0 3 2450 3 3 26.0 5 2150 3 3 28.0 1 2800 3 3 30.0 8 3050 3 3 29.0 10 3200 3 3 26.2 0 2400 3 1 26.5 0 1300 3 3 26.2 3 2400 4 3 25.6 7 2800 4 3 23.0 1 1650 4 3 23.0 0 1800 3 3 25.4 6 2250 4 3 24.2 0 1900 3 2 22.9 0 1600 4 2 26.0 3 2200 3 3 25.4 4 2250 4 3 25.7 0 1200 3 3 25.1 5 2100 4 2 24.5 0 2250 5 3 27.5 0 2900 4 3 23.1 0 1650 4 1 25.9 4 2550 3 3 25.8 0 2300 5 3 27.0 3 2250 3 3 28.5 0 3050 5 1 25.5 0 2750 5 3 23.5 0 1900 3 2 24.0 0 1700 3 1 29.7 5 3850 3 1 26.8 0 2550 5 3 26.7 0 2450 3 1 28.7 0 3200 4 3 23.1 0 1550 3 1 29.0 1 2800 4 3 25.5 0 2250 4 3 26.5 1 1967 4 3 24.5 1 2200 4 3 28.5 1 3000 3 3 28.2 1 2867 3 3 24.5 1 1600 3 3 27.5 1 2550 3 2 24.7 4 2550 3 1 25.2 1 2000 4 3 27.3 1 2900 3 3 26.3 1 2400 3 3 29.0 1 3100 3 3 25.3 2 1900 3 3 26.5 4 2300 3 3 27.8 3 3250 3 3 27.0 6 2500 4 3 25.7 0 2100 3 3 25.0 2 2100 3 3 31.9 2 3325 5 3 23.7 0 1800 5 3 29.3 12 3225 4 3 22.0 0 1400 3 3 25.0 5 2400 4 3 27.0 6 2500 4 3 23.8 6 1800 2 1 30.2 2 3275 4 3 26.2 0 2225 3 3 24.2 2 1650 3 3 27.4 3 2900 3 2 25.4 0 2300 4 3 28.4 3 3200 5 3 22.5 4 1475 3 3 26.2 2 2025 3 1 24.9 6 2300 2 2 24.5 6 1950 3 3 25.1 0 1800 3 1 28.0 4 2900 5 3 25.8 10 2250 3 3 27.9 7 3050 3 3 24.9 0 2200 3 1 28.4 5 3100 4 3 27.2 5 2400 3 2 25.0 6 2250 3 3 27.5 6 2625 3 1 33.5 7 5200 3 3 30.5 3 3325 4 3 29.0 3 2925 3 1 24.3 0 2000 3 3 25.8 0 2400 5 3 25.0 8 2100 3 1 31.7 4 3725 3 3 29.5 4 3025 4 3 24.0 10 1900 3 3 30.0 9 3000 3 3 27.6 4 2850 3 3 26.2 0 2300 3 1 23.1 0 2000 3 1 22.9 0 1600 5 3 24.5 0 1900 3 3 24.7 4 1950 3 3 28.3 0 3200 3 3 23.9 2 1850 4 3 23.8 0 1800 4 2 29.8 4 3500 3 3 26.5 4 2350 3 3 26.0 3 2275 3 3 28.2 8 3050 5 3 25.7 0 2150 3 3 26.5 7 2750 3 3 25.8 0 2200 4 3 24.1 0 1800 4 3 26.2 2 2175 4 3 26.1 3 2750 4 3 29.0 4 3275 2 1 28.0 0 2625 5 3 27.0 0 2625 3 2 24.5 0 2000 ; proc genmod descending; class color; model y = width color / dist=bin link=logit lrci type3 obstats; contrast 'a-d' color 1 0 0 -1; proc genmod descending; model y = width color / dist=bin link=logit; proc genmod descending; model y = width light / dist=bin link=logit; proc genmod descending; class color spine; model y = width weight color spine / dist=bin link=logit type3; proc logistic descending; class color spine / param=ref; model y = width weight color spine / selection=backward lackfit outroc=classif1; proc plot data=classif1; plot _sensit_ * _1mspec_ ; ----------------------------------------------------------------------------------
SAS for CMH Analyses of Clinical Trial Data of Table 6.9
----------------------------------------------------------------------------- data cmh; input center $ treat response count ; datalines; a 1 1 11 a 1 2 25 a 2 1 10 a 2 2 27 b 1 1 16 b 1 2 4 b 2 1 22 b 2 2 10 c 1 1 14 c 1 2 5 c 2 1 7 c 2 2 12 d 1 1 2 d 1 2 14 d 2 1 1 d 2 2 16 e 1 1 6 e 1 2 11 e 2 1 0 e 2 2 12 f 1 1 1 f 1 2 10 f 2 1 0 f 2 2 10 g 1 1 1 g 1 2 4 g 2 1 1 g 2 2 8 h 1 1 4 h 1 2 2 h 2 1 6 h 2 2 1 ; proc freq; weight count; tables center*treat*response / cmh chisq; -----------------------------------------------------------------------------
SAS for Baseline-Category Logit Models with Alligator Data in Table 7.1
---------------------------------------------------------------------- data gator; input lake gender size food count ; datalines; 1 1 1 1 7 1 1 1 2 1 1 1 1 3 0 1 1 1 4 0 1 1 1 5 5 1 1 2 1 4 1 1 2 2 0 1 1 2 3 0 1 1 2 4 1 1 1 2 5 2 1 2 1 1 16 1 2 1 2 3 1 2 1 3 2 1 2 1 4 2 1 2 1 5 3 1 2 2 1 3 1 2 2 2 0 1 2 2 3 1 1 2 2 4 2 1 2 2 5 3 2 1 1 1 2 2 1 1 2 2 2 1 1 3 0 2 1 1 4 0 2 1 1 5 1 2 1 2 1 13 2 1 2 2 7 2 1 2 3 6 2 1 2 4 0 2 1 2 5 0 2 2 1 1 3 2 2 1 2 9 2 2 1 3 1 2 2 1 4 0 2 2 1 5 2 2 2 2 1 0 2 2 2 2 1 2 2 2 3 0 2 2 2 4 1 2 2 2 5 0 3 1 1 1 3 3 1 1 2 7 3 1 1 3 1 3 1 1 4 0 3 1 1 5 1 3 1 2 1 8 3 1 2 2 6 3 1 2 3 6 3 1 2 4 3 3 1 2 5 5 3 2 1 1 2 3 2 1 2 4 3 2 1 3 1 3 2 1 4 1 3 2 1 5 4 3 2 2 1 0 3 2 2 2 1 3 2 2 3 0 3 2 2 4 0 3 2 2 5 0 4 1 1 1 13 4 1 1 2 10 4 1 1 3 0 4 1 1 4 2 4 1 1 5 2 4 1 2 1 9 4 1 2 2 0 4 1 2 3 0 4 1 2 4 1 4 1 2 5 2 4 2 1 1 3 4 2 1 2 9 4 2 1 3 1 4 2 1 4 0 4 2 1 5 1 4 2 2 1 8 4 2 2 2 1 4 2 2 3 0 4 2 2 4 0 4 2 2 5 1 ; proc logistic; freq count; class lake size / param=ref; model food(ref='1') = lake size / link=glogit aggregate scale=none; proc catmod; weight count; population lake size gender; model food = lake size / pred=freq pred=prob; ----------------------------------------------------------------------
SAS for Cumulative Logit Model with Mental Impairment Data of Table 7.5
------------------------------------------------------------------------- data impair; input mental ses life; datalines; 1 1 1 1 1 9 1 1 4 1 1 3 1 0 2 1 1 0 1 0 1 1 1 3 1 1 3 1 1 7 1 0 1 1 0 2 2 1 5 2 0 6 2 1 3 2 0 1 2 1 8 2 1 2 2 0 5 2 1 5 2 1 9 2 0 3 2 1 3 2 1 1 3 0 0 3 1 4 3 0 3 3 0 9 3 1 6 3 0 4 3 0 3 4 1 8 4 1 2 4 1 7 4 0 5 4 0 4 4 0 4 4 1 8 4 0 8 4 0 9 ; proc genmod ; model mental = life ses / dist=multinomial link=clogit lrci type3; proc logistic; model mental = life ses / link=probit; -------------------------------------------------------------------------
SAS for Adjacent-Categories Logit and Mean Response Models and CMH Analyses of Job Satisfaction Data in Table 7.8
------------------------------------------------------------------------
data jobsat;
input gender income satisf count ;
count2 = count + .01;
datalines;
1 1 1 1
1 1 2 3
1 1 3 11
1 1 4 2
1 2 1 2
1 2 2 3
1 2 3 17
1 2 4 3
1 3 1 0
1 3 2 1
1 3 3 8
1 3 4 5
1 4 1 0
1 4 2 2
1 4 3 4
1 4 4 2
0 1 1 1
0 1 2 1
0 1 3 2
0 1 4 1
0 2 1 0
0 2 2 3
0 2 3 5
0 2 4 1
0 3 1 0
0 3 2 0
0 3 3 7
0 3 4 3
0 4 1 0
0 4 2 1
0 4 3 9
0 4 4 6
;
proc catmod order=data; * ML analysis of adj-cat logit (ACL) model;
weight count;
population gender income;
model satisf =
(1 0 0 3 3, 0 1 0 2 2, 0 0 1 1 1,
1 0 0 6 3, 0 1 0 4 2, 0 0 1 2 1,
1 0 0 9 3, 0 1 0 6 2, 0 0 1 3 1,
1 0 0 12 3, 0 1 0 8 2, 0 0 1 4 1,
1 0 0 3 0, 0 1 0 2 0, 0 0 1 1 0,
1 0 0 6 0, 0 1 0 4 0, 0 0 1 2 0,
1 0 0 9 0, 0 1 0 6 0, 0 0 1 3 0,
1 0 0 12 0, 0 1 0 8 0, 0 0 1 4 0)
/ ml pred=freq;
proc catmod order=data; weight count2; * WLS analysis of ACL model;
response alogits; population gender income; direct gender income;
model satisf = _response_ gender income;
proc catmod; weight count; * mean response model;
population gender income; response mean; direct gender income;
model satisf = gender income / covb;
proc freq; weight count;
tables gender*income*satisf / cmh scores=table;
------------------------------------------------------------------------
SAS for Loglinear Models with Drug Survey Data of Table 8.3
------------------------------------------------------------------------- data drugs; input a c m count ; datalines; 1 1 1 911 1 1 2 538 1 2 1 44 1 2 2 456 2 1 1 3 2 1 2 43 2 2 1 2 2 2 2 279 ; proc genmod; class a c m; model count = a c m a*m a*c c*m / dist=poi lrci type3 residuals obstats; --------------------------------------------------------------------------
----------------------------------------------------------------------- data rake; input school atti count ; log_c = log(count); pseudo = 100/3; cards; 1 1 209 1 2 101 1 3 237 2 1 151 2 2 126 2 3 426 3 1 16 3 2 21 3 3 138 ; proc genmod; class school atti; model pseudo = school atti / dist=poi link=log offset=log_c obstats; -----------------------------------------------------------------------
------------------------------------------------------------------- data sex; input premar birth u v count ; assoc = u*v ; datalines; 1 4 1 1 38 1 3 1 2 60 1 2 1 4 68 1 1 1 5 81 2 4 2 1 14 2 3 2 2 29 2 2 2 4 26 2 1 2 5 24 3 4 4 1 42 3 3 4 2 74 3 2 4 4 41 3 1 4 5 18 4 4 5 1 157 4 3 5 2 161 4 2 5 4 57 4 1 5 5 36 ; proc genmod; class premar birth; model count = premar birth assoc / dist=poi link=log; proc genmod; class premar birth; model count = premar birth premar*v / dist=poi link=log; -------------------------------------------------------------------
SAS for McNemar's Test and Comparing Proportions for Matched Samples in Table 10.1
--------------------------------------------------------------
data matched;
input first second count ;
datalines;
1 1 794
1 2 150
2 1 86
2 2 570
;
proc freq; weight count;
tables first*second / agree; exact mcnem;
proc catmod; weight count;
response marginals;
model first*second = (1 0 ,
1 1 ) ;
--------------------------------------------------------------
SAS for Testing Marginal Homogeneity with Migration Data of Table 10.6
--------------------------------------------------------------------------
data migrate;
input then $ now $ count m11 m12 m13 m21 m22 m23 m31 m32 m33 m44 m1 m2 m3;
datalines;
ne ne 11607 1 0 0 0 0 0 0 0 0 0 0 0 0
ne mw 100 0 1 0 0 0 0 0 0 0 0 0 0 0
ne s 366 0 0 1 0 0 0 0 0 0 0 0 0 0
ne w 124 -1 -1 -1 0 0 0 0 0 0 0 1 0 0
mw ne 87 0 0 0 1 0 0 0 0 0 0 0 0 0
mw mw 13677 0 0 0 0 1 0 0 0 0 0 0 0 0
mw s 515 0 0 0 0 0 1 0 0 0 0 0 0 0
mw w 302 0 0 0 -1 -1 -1 0 0 0 0 0 1 0
s ne 172 0 0 0 0 0 0 1 0 0 0 0 0 0
s mw 225 0 0 0 0 0 0 0 1 0 0 0 0 0
s s 17819 0 0 0 0 0 0 0 0 1 0 0 0 0
s w 270 0 0 0 0 0 0 -1 -1 -1 0 0 0 1
w ne 63 -1 0 0 -1 0 0 -1 0 0 0 1 0 0
w mw 176 0 -1 0 0 -1 0 0 -1 0 0 0 1 0
w s 286 0 0 -1 0 0 -1 0 0 -1 0 0 0 1
w w 10192 0 0 0 0 0 0 0 0 0 1 0 0 0
;
proc genmod;
model count = m11 m12 m13 m21 m22 m23 m31 m32 m33 m44 m1 m2 m3
/ dist=poi link=identity;
proc catmod; weight count; response marginals;
model then*now = _response_ / freq;
repeated time 2;
--------------------------------------------------------------------------
SAS for Square-Table Analyses of Table 10.5
-------------------------------------------------------------------------- data sex; input premar extramar symm qi count; unif = premar*extramar; datalines; 1 1 1 1 144 1 2 2 5 2 1 3 3 5 0 1 4 4 5 0 2 1 2 5 33 2 2 5 2 4 2 3 6 5 2 2 4 7 5 0 3 1 3 5 84 3 2 6 5 14 3 3 8 3 6 3 4 9 5 1 4 1 4 5 126 4 2 7 5 29 4 3 9 5 25 4 4 10 4 5 ; proc genmod; class symm; model count = symm / dist=poi link=log; * symmetry; proc genmod; class extramar premar symm; model count = symm extramar premar / dist=poi link=log; *QS; proc genmod; class symm; model count = symm extramar premar / dist=poi link=log; * ordinal QS; proc genmod; class extramar premar qi; model count = extramar premar qi / dist=poi link=log; * quasi indep; proc genmod; class extramar premar; model count = extramar premar unif / dist=poi link=log; data sex2; input score below above @@; trials = below + above; datalines; 1 33 2 1 14 2 1 25 1 2 84 0 2 29 0 3 126 0 ; proc genmod data=sex2; model above/trials = score / dist=bin link=logit noint; proc genmod data=sex2; model above/trials = / dist=bin link=logit noint; proc genmod data=sex2; model above/trials = / dist=bin link=logit; --------------------------------------------------------------------------
SAS for Bradley-Terry Model with Table 10.10
----------------------------------------------------------------------------- data baseball; input wins games milw detr toro newy bost clev balt; datalines; 7 13 1 -1 0 0 0 0 0 9 13 1 0 -1 0 0 0 0 7 13 1 0 0 -1 0 0 0 7 13 1 0 0 0 -1 0 0 9 13 1 0 0 0 0 -1 0 11 13 1 0 0 0 0 0 -1 7 13 0 1 -1 0 0 0 0 5 13 0 1 0 -1 0 0 0 11 13 0 1 0 0 -1 0 0 9 13 0 1 0 0 0 -1 0 9 13 0 1 0 0 0 0 -1 7 13 0 0 1 -1 0 0 0 7 13 0 0 1 0 -1 0 0 8 13 0 0 1 0 0 -1 0 12 13 0 0 1 0 0 0 -1 6 13 0 0 0 1 -1 0 0 7 13 0 0 0 1 0 -1 0 10 13 0 0 0 1 0 0 -1 7 13 0 0 0 0 1 -1 0 12 13 0 0 0 0 1 0 -1 6 13 0 0 0 0 0 1 -1 ; proc genmod; model wins/games = milw detr toro newy bost clev balt / dist=bin link=logit noint covb; -----------------------------------------------------------------------------
SAS for Testing Marginal Homogeneity with Crossover Study of Table 11.1
-----------------------------------------------------------------------------
data crossove;
input a b c count m111 m11p m1p1 mp11 m1pp m222 ;
datalines;
1 1 1 6 1 0 0 0 0 0
1 1 2 16 -1 1 0 0 0 0
1 2 1 2 -1 0 1 0 0 0
1 2 2 4 1 -1 -1 0 1 0
2 1 1 2 -1 0 0 1 0 0
2 1 2 4 1 -1 0 -1 1 0
2 2 1 6 1 0 -1 -1 1 0
2 2 2 6 0 0 0 0 0 1
;
proc genmod;
model count = m111 m11p m1p1 mp11 m1pp m222 / dist=poi link=identity;
proc catmod; weight count; response marginals;
model a*b*c = _response_ / freq;
repeated drug 3;
-----------------------------------------------------------------------------
SAS for Marginal Modeling of Depression Data of Table 11.2
----------------------------------------------------------------------------- data depress; input case diagnose treat time outcome ; * outcome=1 is normal; datalines; 1 0 0 0 1 1 0 0 1 1 1 0 0 2 1 2 0 0 0 1 2 0 0 1 1 2 0 0 2 1 3 0 0 0 1 3 0 0 1 1 3 0 0 2 1 4 0 0 0 1 4 0 0 1 1 4 0 0 2 1 5 0 0 0 1 5 0 0 1 1 5 0 0 2 1 6 0 0 0 1 6 0 0 1 1 6 0 0 2 1 7 0 0 0 1 7 0 0 1 1 7 0 0 2 1 8 0 0 0 1 8 0 0 1 1 8 0 0 2 1 9 0 0 0 1 9 0 0 1 1 9 0 0 2 1 10 0 0 0 1 10 0 0 1 1 10 0 0 2 1 11 0 0 0 1 11 0 0 1 1 11 0 0 2 1 12 0 0 0 1 12 0 0 1 1 12 0 0 2 1 13 0 0 0 1 13 0 0 1 1 13 0 0 2 1 14 0 0 0 1 14 0 0 1 1 14 0 0 2 1 15 0 0 0 1 15 0 0 1 1 15 0 0 2 1 16 0 0 0 1 16 0 0 1 1 16 0 0 2 1 17 0 0 0 1 17 0 0 1 1 17 0 0 2 0 18 0 0 0 1 18 0 0 1 1 18 0 0 2 0 19 0 0 0 1 19 0 0 1 1 19 0 0 2 0 20 0 0 0 1 20 0 0 1 1 20 0 0 2 0 21 0 0 0 1 21 0 0 1 1 21 0 0 2 0 22 0 0 0 1 22 0 0 1 1 22 0 0 2 0 23 0 0 0 1 23 0 0 1 1 23 0 0 2 0 24 0 0 0 1 24 0 0 1 1 24 0 0 2 0 25 0 0 0 1 25 0 0 1 1 25 0 0 2 0 26 0 0 0 1 26 0 0 1 1 26 0 0 2 0 27 0 0 0 1 27 0 0 1 1 27 0 0 2 0 28 0 0 0 1 28 0 0 1 1 28 0 0 2 0 29 0 0 0 1 29 0 0 1 1 29 0 0 2 0 30 0 0 0 1 30 0 0 1 0 30 0 0 2 1 31 0 0 0 1 31 0 0 1 0 31 0 0 2 1 32 0 0 0 1 32 0 0 1 0 32 0 0 2 1 33 0 0 0 1 33 0 0 1 0 33 0 0 2 1 34 0 0 0 1 34 0 0 1 0 34 0 0 2 1 35 0 0 0 1 35 0 0 1 0 35 0 0 2 1 36 0 0 0 1 36 0 0 1 0 36 0 0 2 1 37 0 0 0 1 37 0 0 1 0 37 0 0 2 1 38 0 0 0 1 38 0 0 1 0 38 0 0 2 1 39 0 0 0 1 39 0 0 1 0 39 0 0 2 0 40 0 0 0 1 40 0 0 1 0 40 0 0 2 0 41 0 0 0 1 41 0 0 1 0 41 0 0 2 0 42 0 0 0 0 42 0 0 1 1 42 0 0 2 1 43 0 0 0 0 43 0 0 1 1 43 0 0 2 1 44 0 0 0 0 44 0 0 1 1 44 0 0 2 1 45 0 0 0 0 45 0 0 1 1 45 0 0 2 1 46 0 0 0 0 46 0 0 1 1 46 0 0 2 1 47 0 0 0 0 47 0 0 1 1 47 0 0 2 1 48 0 0 0 0 48 0 0 1 1 48 0 0 2 1 49 0 0 0 0 49 0 0 1 1 49 0 0 2 1 50 0 0 0 0 50 0 0 1 1 50 0 0 2 1 51 0 0 0 0 51 0 0 1 1 51 0 0 2 1 52 0 0 0 0 52 0 0 1 1 52 0 0 2 1 53 0 0 0 0 53 0 0 1 1 53 0 0 2 1 54 0 0 0 0 54 0 0 1 1 54 0 0 2 1 55 0 0 0 0 55 0 0 1 1 55 0 0 2 1 56 0 0 0 0 56 0 0 1 1 56 0 0 2 0 57 0 0 0 0 57 0 0 1 1 57 0 0 2 0 58 0 0 0 0 58 0 0 1 1 58 0 0 2 0 59 0 0 0 0 59 0 0 1 1 59 0 0 2 0 60 0 0 0 0 60 0 0 1 0 60 0 0 2 1 61 0 0 0 0 61 0 0 1 0 61 0 0 2 1 62 0 0 0 0 62 0 0 1 0 62 0 0 2 1 63 0 0 0 0 63 0 0 1 0 63 0 0 2 1 64 0 0 0 0 64 0 0 1 0 64 0 0 2 1 65 0 0 0 0 65 0 0 1 0 65 0 0 2 1 66 0 0 0 0 66 0 0 1 0 66 0 0 2 1 67 0 0 0 0 67 0 0 1 0 67 0 0 2 1 68 0 0 0 0 68 0 0 1 0 68 0 0 2 1 69 0 0 0 0 69 0 0 1 0 69 0 0 2 1 70 0 0 0 0 70 0 0 1 0 70 0 0 2 1 71 0 0 0 0 71 0 0 1 0 71 0 0 2 1 72 0 0 0 0 72 0 0 1 0 72 0 0 2 1 73 0 0 0 0 73 0 0 1 0 73 0 0 2 1 74 0 0 0 0 74 0 0 1 0 74 0 0 2 1 75 0 0 0 0 75 0 0 1 0 75 0 0 2 0 336 0 0 0 0 336 0 0 1 0 336 0 0 2 0 337 0 0 0 0 337 0 0 1 0 337 0 0 2 0 338 0 0 0 0 338 0 0 1 0 338 0 0 2 0 339 0 0 0 0 339 0 0 1 0 339 0 0 2 0 340 0 0 0 0 340 0 0 1 0 340 0 0 2 0 76 0 1 0 1 76 0 1 1 1 76 0 1 2 1 77 0 1 0 1 77 0 1 1 1 77 0 1 2 1 78 0 1 0 1 78 0 1 1 1 78 0 1 2 1 79 0 1 0 1 79 0 1 1 1 79 0 1 2 1 80 0 1 0 1 80 0 1 1 1 80 0 1 2 1 81 0 1 0 1 81 0 1 1 1 81 0 1 2 1 82 0 1 0 1 82 0 1 1 1 82 0 1 2 1 83 0 1 0 1 83 0 1 1 1 83 0 1 2 1 84 0 1 0 1 84 0 1 1 1 84 0 1 2 1 85 0 1 0 1 85 0 1 1 1 85 0 1 2 1 86 0 1 0 1 86 0 1 1 1 86 0 1 2 1 87 0 1 0 1 87 0 1 1 1 87 0 1 2 1 88 0 1 0 1 88 0 1 1 1 88 0 1 2 1 89 0 1 0 1 89 0 1 1 1 89 0 1 2 1 90 0 1 0 1 90 0 1 1 1 90 0 1 2 1 91 0 1 0 1 91 0 1 1 1 91 0 1 2 1 92 0 1 0 1 92 0 1 1 1 92 0 1 2 1 93 0 1 0 1 93 0 1 1 1 93 0 1 2 1 94 0 1 0 1 94 0 1 1 1 94 0 1 2 1 95 0 1 0 1 95 0 1 1 1 95 0 1 2 1 96 0 1 0 1 96 0 1 1 1 96 0 1 2 1 97 0 1 0 1 97 0 1 1 1 97 0 1 2 1 98 0 1 0 1 98 0 1 1 1 98 0 1 2 1 99 0 1 0 1 99 0 1 1 1 99 0 1 2 1 100 0 1 0 1 100 0 1 1 1 100 0 1 2 1 101 0 1 0 1 101 0 1 1 1 101 0 1 2 1 102 0 1 0 1 102 0 1 1 1 102 0 1 2 1 103 0 1 0 1 103 0 1 1 1 103 0 1 2 1 104 0 1 0 1 104 0 1 1 1 104 0 1 2 1 105 0 1 0 1 105 0 1 1 1 105 0 1 2 1 106 0 1 0 1 106 0 1 1 1 106 0 1 2 1 107 0 1 0 1 107 0 1 1 0 107 0 1 2 1 108 0 1 0 1 108 0 1 1 0 108 0 1 2 1 109 0 1 0 1 109 0 1 1 0 109 0 1 2 1 110 0 1 0 1 110 0 1 1 0 110 0 1 2 1 111 0 1 0 1 111 0 1 1 0 111 0 1 2 1 112 0 1 0 1 112 0 1 1 0 112 0 1 2 1 113 0 1 0 0 113 0 1 1 1 113 0 1 2 1 114 0 1 0 0 114 0 1 1 1 114 0 1 2 1 115 0 1 0 0 115 0 1 1 1 115 0 1 2 1 116 0 1 0 0 116 0 1 1 1 116 0 1 2 1 117 0 1 0 0 117 0 1 1 1 117 0 1 2 1 118 0 1 0 0 118 0 1 1 1 118 0 1 2 1 119 0 1 0 0 119 0 1 1 1 119 0 1 2 1 120 0 1 0 0 120 0 1 1 1 120 0 1 2 1 121 0 1 0 0 121 0 1 1 1 121 0 1 2 1 122 0 1 0 0 122 0 1 1 1 122 0 1 2 1 123 0 1 0 0 123 0 1 1 1 123 0 1 2 1 124 0 1 0 0 124 0 1 1 1 124 0 1 2 1 125 0 1 0 0 125 0 1 1 1 125 0 1 2 1 126 0 1 0 0 126 0 1 1 1 126 0 1 2 1 127 0 1 0 0 127 0 1 1 1 127 0 1 2 1 128 0 1 0 0 128 0 1 1 1 128 0 1 2 1 129 0 1 0 0 129 0 1 1 1 129 0 1 2 1 130 0 1 0 0 130 0 1 1 1 130 0 1 2 1 131 0 1 0 0 131 0 1 1 1 131 0 1 2 1 132 0 1 0 0 132 0 1 1 1 132 0 1 2 1 133 0 1 0 0 133 0 1 1 1 133 0 1 2 1 134 0 1 0 0 134 0 1 1 1 134 0 1 2 1 135 0 1 0 0 135 0 1 1 1 135 0 1 2 0 136 0 1 0 0 136 0 1 1 1 136 0 1 2 0 137 0 1 0 0 137 0 1 1 0 137 0 1 2 1 138 0 1 0 0 138 0 1 1 0 138 0 1 2 1 139 0 1 0 0 139 0 1 1 0 139 0 1 2 1 140 0 1 0 0 140 0 1 1 0 140 0 1 2 1 141 0 1 0 0 141 0 1 1 0 141 0 1 2 1 142 0 1 0 0 142 0 1 1 0 142 0 1 2 1 143 0 1 0 0 143 0 1 1 0 143 0 1 2 1 144 0 1 0 0 144 0 1 1 0 144 0 1 2 1 145 0 1 0 0 145 0 1 1 0 145 0 1 2 1 146 1 0 0 1 146 1 0 1 1 146 1 0 2 1 147 1 0 0 1 147 1 0 1 1 147 1 0 2 1 148 1 0 0 1 148 1 0 1 1 148 1 0 2 0 149 1 0 0 1 149 1 0 1 1 149 1 0 2 0 150 1 0 0 1 150 1 0 1 0 150 1 0 2 1 151 1 0 0 1 151 1 0 1 0 151 1 0 2 1 152 1 0 0 1 152 1 0 1 0 152 1 0 2 1 153 1 0 0 1 153 1 0 1 0 153 1 0 2 1 154 1 0 0 1 154 1 0 1 0 154 1 0 2 1 155 1 0 0 1 155 1 0 1 0 155 1 0 2 1 156 1 0 0 1 156 1 0 1 0 156 1 0 2 1 157 1 0 0 1 157 1 0 1 0 157 1 0 2 1 158 1 0 0 1 158 1 0 1 0 158 1 0 2 0 159 1 0 0 1 159 1 0 1 0 159 1 0 2 0 160 1 0 0 1 160 1 0 1 0 160 1 0 2 0 161 1 0 0 1 161 1 0 1 0 161 1 0 2 0 162 1 0 0 1 162 1 0 1 0 162 1 0 2 0 163 1 0 0 1 163 1 0 1 0 163 1 0 2 0 164 1 0 0 1 164 1 0 1 0 164 1 0 2 0 165 1 0 0 1 165 1 0 1 0 165 1 0 2 0 166 1 0 0 1 166 1 0 1 0 166 1 0 2 0 167 1 0 0 0 167 1 0 1 1 167 1 0 2 1 168 1 0 0 0 168 1 0 1 1 168 1 0 2 1 169 1 0 0 0 169 1 0 1 1 169 1 0 2 1 170 1 0 0 0 170 1 0 1 1 170 1 0 2 1 171 1 0 0 0 171 1 0 1 1 171 1 0 2 1 172 1 0 0 0 172 1 0 1 1 172 1 0 2 1 173 1 0 0 0 173 1 0 1 1 173 1 0 2 1 174 1 0 0 0 174 1 0 1 1 174 1 0 2 1 175 1 0 0 0 175 1 0 1 1 175 1 0 2 1 176 1 0 0 0 176 1 0 1 1 176 1 0 2 0 177 1 0 0 0 177 1 0 1 1 177 1 0 2 0 178 1 0 0 0 178 1 0 1 1 178 1 0 2 0 179 1 0 0 0 179 1 0 1 1 179 1 0 2 0 180 1 0 0 0 180 1 0 1 1 180 1 0 2 0 181 1 0 0 0 181 1 0 1 1 181 1 0 2 0 182 1 0 0 0 182 1 0 1 1 182 1 0 2 0 183 1 0 0 0 183 1 0 1 1 183 1 0 2 0 184 1 0 0 0 184 1 0 1 1 184 1 0 2 0 185 1 0 0 0 185 1 0 1 1 185 1 0 2 0 186 1 0 0 0 186 1 0 1 1 186 1 0 2 0 187 1 0 0 0 187 1 0 1 1 187 1 0 2 0 188 1 0 0 0 188 1 0 1 1 188 1 0 2 0 189 1 0 0 0 189 1 0 1 1 189 1 0 2 0 190 1 0 0 0 190 1 0 1 1 190 1 0 2 0 191 1 0 0 0 191 1 0 1 0 191 1 0 2 1 192 1 0 0 0 192 1 0 1 0 192 1 0 2 1 193 1 0 0 0 193 1 0 1 0 193 1 0 2 1 194 1 0 0 0 194 1 0 1 0 194 1 0 2 1 195 1 0 0 0 195 1 0 1 0 195 1 0 2 1 196 1 0 0 0 196 1 0 1 0 196 1 0 2 1 197 1 0 0 0 197 1 0 1 0 197 1 0 2 1 198 1 0 0 0 198 1 0 1 0 198 1 0 2 1 199 1 0 0 0 199 1 0 1 0 199 1 0 2 1 200 1 0 0 0 200 1 0 1 0 200 1 0 2 1 201 1 0 0 0 201 1 0 1 0 201 1 0 2 1 202 1 0 0 0 202 1 0 1 0 202 1 0 2 1 203 1 0 0 0 203 1 0 1 0 203 1 0 2 1 204 1 0 0 0 204 1 0 1 0 204 1 0 2 1 205 1 0 0 0 205 1 0 1 0 205 1 0 2 1 206 1 0 0 0 206 1 0 1 0 206 1 0 2 1 207 1 0 0 0 207 1 0 1 0 207 1 0 2 1 208 1 0 0 0 208 1 0 1 0 208 1 0 2 1 209 1 0 0 0 209 1 0 1 0 209 1 0 2 1 210 1 0 0 0 210 1 0 1 0 210 1 0 2 1 211 1 0 0 0 211 1 0 1 0 211 1 0 2 1 212 1 0 0 0 212 1 0 1 0 212 1 0 2 1 213 1 0 0 0 213 1 0 1 0 213 1 0 2 1 214 1 0 0 0 214 1 0 1 0 214 1 0 2 1 215 1 0 0 0 215 1 0 1 0 215 1 0 2 1 216 1 0 0 0 216 1 0 1 0 216 1 0 2 1 217 1 0 0 0 217 1 0 1 0 217 1 0 2 1 218 1 0 0 0 218 1 0 1 0 218 1 0 2 0 219 1 0 0 0 219 1 0 1 0 219 1 0 2 0 220 1 0 0 0 220 1 0 1 0 220 1 0 2 0 221 1 0 0 0 221 1 0 1 0 221 1 0 2 0 222 1 0 0 0 222 1 0 1 0 222 1 0 2 0 223 1 0 0 0 223 1 0 1 0 223 1 0 2 0 224 1 0 0 0 224 1 0 1 0 224 1 0 2 0 225 1 0 0 0 225 1 0 1 0 225 1 0 2 0 226 1 0 0 0 226 1 0 1 0 226 1 0 2 0 227 1 0 0 0 227 1 0 1 0 227 1 0 2 0 228 1 0 0 0 228 1 0 1 0 228 1 0 2 0 229 1 0 0 0 229 1 0 1 0 229 1 0 2 0 230 1 0 0 0 230 1 0 1 0 230 1 0 2 0 231 1 0 0 0 231 1 0 1 0 231 1 0 2 0 232 1 0 0 0 232 1 0 1 0 232 1 0 2 0 233 1 0 0 0 233 1 0 1 0 233 1 0 2 0 234 1 0 0 0 234 1 0 1 0 234 1 0 2 0 235 1 0 0 0 235 1 0 1 0 235 1 0 2 0 236 1 0 0 0 236 1 0 1 0 236 1 0 2 0 237 1 0 0 0 237 1 0 1 0 237 1 0 2 0 238 1 0 0 0 238 1 0 1 0 238 1 0 2 0 239 1 0 0 0 239 1 0 1 0 239 1 0 2 0 240 1 0 0 0 240 1 0 1 0 240 1 0 2 0 241 1 0 0 0 241 1 0 1 0 241 1 0 2 0 242 1 0 0 0 242 1 0 1 0 242 1 0 2 0 243 1 0 0 0 243 1 0 1 0 243 1 0 2 0 244 1 0 0 0 244 1 0 1 0 244 1 0 2 0 245 1 0 0 0 245 1 0 1 0 245 1 0 2 0 246 1 1 0 1 246 1 1 1 1 246 1 1 2 1 247 1 1 0 1 247 1 1 1 1 247 1 1 2 1 248 1 1 0 1 248 1 1 1 1 248 1 1 2 1 249 1 1 0 1 249 1 1 1 1 249 1 1 2 1 250 1 1 0 1 250 1 1 1 1 250 1 1 2 1 251 1 1 0 1 251 1 1 1 1 251 1 1 2 1 252 1 1 0 1 252 1 1 1 1 252 1 1 2 1 253 1 1 0 1 253 1 1 1 1 253 1 1 2 0 254 1 1 0 1 254 1 1 1 1 254 1 1 2 0 255 1 1 0 1 255 1 1 1 0 255 1 1 2 1 256 1 1 0 1 256 1 1 1 0 256 1 1 2 1 257 1 1 0 1 257 1 1 1 0 257 1 1 2 1 258 1 1 0 1 258 1 1 1 0 258 1 1 2 1 259 1 1 0 1 259 1 1 1 0 259 1 1 2 1 260 1 1 0 1 260 1 1 1 0 260 1 1 2 0 261 1 1 0 1 261 1 1 1 0 261 1 1 2 0 262 1 1 0 0 262 1 1 1 1 262 1 1 2 1 263 1 1 0 0 263 1 1 1 1 263 1 1 2 1 264 1 1 0 0 264 1 1 1 1 264 1 1 2 1 265 1 1 0 0 265 1 1 1 1 265 1 1 2 1 266 1 1 0 0 266 1 1 1 1 266 1 1 2 1 267 1 1 0 0 267 1 1 1 1 267 1 1 2 1 268 1 1 0 0 268 1 1 1 1 268 1 1 2 1 269 1 1 0 0 269 1 1 1 1 269 1 1 2 1 270 1 1 0 0 270 1 1 1 1 270 1 1 2 1 271 1 1 0 0 271 1 1 1 1 271 1 1 2 1 272 1 1 0 0 272 1 1 1 1 272 1 1 2 1 273 1 1 0 0 273 1 1 1 1 273 1 1 2 1 274 1 1 0 0 274 1 1 1 1 274 1 1 2 1 275 1 1 0 0 275 1 1 1 1 275 1 1 2 1 276 1 1 0 0 276 1 1 1 1 276 1 1 2 1 277 1 1 0 0 277 1 1 1 1 277 1 1 2 1 278 1 1 0 0 278 1 1 1 1 278 1 1 2 1 279 1 1 0 0 279 1 1 1 1 279 1 1 2 1 280 1 1 0 0 280 1 1 1 1 280 1 1 2 1 281 1 1 0 0 281 1 1 1 1 281 1 1 2 1 282 1 1 0 0 282 1 1 1 1 282 1 1 2 1 283 1 1 0 0 283 1 1 1 1 283 1 1 2 1 284 1 1 0 0 284 1 1 1 1 284 1 1 2 1 285 1 1 0 0 285 1 1 1 1 285 1 1 2 1 286 1 1 0 0 286 1 1 1 1 286 1 1 2 1 287 1 1 0 0 287 1 1 1 1 287 1 1 2 1 288 1 1 0 0 288 1 1 1 1 288 1 1 2 1 289 1 1 0 0 289 1 1 1 1 289 1 1 2 1 290 1 1 0 0 290 1 1 1 1 290 1 1 2 1 291 1 1 0 0 291 1 1 1 1 291 1 1 2 1 292 1 1 0 0 292 1 1 1 1 292 1 1 2 1 293 1 1 0 0 293 1 1 1 1 293 1 1 2 0 294 1 1 0 0 294 1 1 1 1 294 1 1 2 0 295 1 1 0 0 295 1 1 1 1 295 1 1 2 0 296 1 1 0 0 296 1 1 1 1 296 1 1 2 0 297 1 1 0 0 297 1 1 1 1 297 1 1 2 0 298 1 1 0 0 298 1 1 1 0 298 1 1 2 1 299 1 1 0 0 299 1 1 1 0 299 1 1 2 1 300 1 1 0 0 300 1 1 1 0 300 1 1 2 1 301 1 1 0 0 301 1 1 1 0 301 1 1 2 1 302 1 1 0 0 302 1 1 1 0 302 1 1 2 1 303 1 1 0 0 303 1 1 1 0 303 1 1 2 1 304 1 1 0 0 304 1 1 1 0 304 1 1 2 1 305 1 1 0 0 305 1 1 1 0 305 1 1 2 1 306 1 1 0 0 306 1 1 1 0 306 1 1 2 1 307 1 1 0 0 307 1 1 1 0 307 1 1 2 1 308 1 1 0 0 308 1 1 1 0 308 1 1 2 1 309 1 1 0 0 309 1 1 1 0 309 1 1 2 1 310 1 1 0 0 310 1 1 1 0 310 1 1 2 1 311 1 1 0 0 311 1 1 1 0 311 1 1 2 1 312 1 1 0 0 312 1 1 1 0 312 1 1 2 1 313 1 1 0 0 313 1 1 1 0 313 1 1 2 1 314 1 1 0 0 314 1 1 1 0 314 1 1 2 1 315 1 1 0 0 315 1 1 1 0 315 1 1 2 1 316 1 1 0 0 316 1 1 1 0 316 1 1 2 1 317 1 1 0 0 317 1 1 1 0 317 1 1 2 1 318 1 1 0 0 318 1 1 1 0 318 1 1 2 1 319 1 1 0 0 319 1 1 1 0 319 1 1 2 1 320 1 1 0 0 320 1 1 1 0 320 1 1 2 1 321 1 1 0 0 321 1 1 1 0 321 1 1 2 1 322 1 1 0 0 322 1 1 1 0 322 1 1 2 1 323 1 1 0 0 323 1 1 1 0 323 1 1 2 1 324 1 1 0 0 324 1 1 1 0 324 1 1 2 1 325 1 1 0 0 325 1 1 1 0 325 1 1 2 1 326 1 1 0 0 326 1 1 1 0 326 1 1 2 1 327 1 1 0 0 327 1 1 1 0 327 1 1 2 1 328 1 1 0 0 328 1 1 1 0 328 1 1 2 1 329 1 1 0 0 329 1 1 1 0 329 1 1 2 1 330 1 1 0 0 330 1 1 1 0 330 1 1 2 0 331 1 1 0 0 331 1 1 1 0 331 1 1 2 0 332 1 1 0 0 332 1 1 1 0 332 1 1 2 0 333 1 1 0 0 333 1 1 1 0 333 1 1 2 0 334 1 1 0 0 334 1 1 1 0 334 1 1 2 0 335 1 1 0 0 335 1 1 1 0 335 1 1 2 0 ; proc genmod descending; class case; model outcome = diagnose treat time treat*time / dist=bin link=logit type3; repeated subject=case / type=exch corrw; proc nlmixed qpoints=200; parms alpha=-.03 beta1=-1.3 beta2=-.06 beta3=.48 beta4=1.02 sigma=.066; eta = alpha + beta1*diagnose + beta2*treat + beta3*time + beta4*treat*time + u; p = exp(eta)/(1 + exp(eta)); model outcome ~ binary(p); random u ~ normal(0, sigma*sigma) subject = case; -----------------------------------------------------------------------------
SAS for GEE and Random Intercept Cumulative Logit Analyses of Insomnia data of Table 11.4
--------------------------------------------------------------------------
data francom;
input case treat occasion outcome count;
datalines;
1 1 0 1 7
1 1 1 1 7
2 1 0 1 7
2 1 1 1 7
3 1 0 1 7
3 1 1 1 7
4 1 0 1 7
4 1 1 1 7
5 1 0 1 7
5 1 1 1 7
6 1 0 1 7
6 1 1 1 7
7 1 0 1 7
7 1 1 1 7
8 1 0 1 4
8 1 1 2 4
9 1 0 1 4
9 1 1 2 4
10 1 0 1 4
10 1 1 2 4
11 1 0 1 4
11 1 1 2 4
12 1 0 1 1
12 1 1 3 1
13 1 0 2 11
13 1 1 1 11
14 1 0 2 11
14 1 1 1 11
15 1 0 2 11
15 1 1 1 11
16 1 0 2 11
16 1 1 1 11
17 1 0 2 11
17 1 1 1 11
18 1 0 2 11
18 1 1 1 11
19 1 0 2 11
19 1 1 1 11
20 1 0 2 11
20 1 1 1 11
21 1 0 2 11
21 1 1 1 11
22 1 0 2 11
22 1 1 1 11
23 1 0 2 11
23 1 1 1 11
24 1 0 2 5
24 1 1 2 5
25 1 0 2 5
25 1 1 2 5
26 1 0 2 5
26 1 1 2 5
27 1 0 2 5
27 1 1 2 5
28 1 0 2 5
28 1 1 2 5
29 1 0 2 2
29 1 1 3 2
30 1 0 2 2
30 1 1 3 2
31 1 0 2 2
31 1 1 4 2
32 1 0 2 2
32 1 1 4 2
33 1 0 3 13
33 1 1 1 13
34 1 0 3 13
34 1 1 1 13
35 1 0 3 13
35 1 1 1 13
36 1 0 3 13
36 1 1 1 13
37 1 0 3 13
37 1 1 1 13
38 1 0 3 13
38 1 1 1 13
39 1 0 3 13
39 1 1 1 13
40 1 0 3 13
40 1 1 1 13
41 1 0 3 13
41 1 1 1 13
42 1 0 3 13
42 1 1 1 13
43 1 0 3 13
43 1 1 1 13
44 1 0 3 13
44 1 1 1 13
45 1 0 3 13
45 1 1 1 13
46 1 0 3 23
46 1 1 2 23
47 1 0 3 23
47 1 1 2 23
48 1 0 3 23
48 1 1 2 23
49 1 0 3 23
49 1 1 2 23
50 1 0 3 23
50 1 1 2 23
51 1 0 3 23
51 1 1 2 23
52 1 0 3 23
52 1 1 2 23
53 1 0 3 23
53 1 1 2 23
54 1 0 3 23
54 1 1 2 23
55 1 0 3 23
55 1 1 2 23
56 1 0 3 23
56 1 1 2 23
57 1 0 3 23
57 1 1 2 23
58 1 0 3 23
58 1 1 2 23
59 1 0 3 23
59 1 1 2 23
60 1 0 3 23
60 1 1 2 23
61 1 0 3 23
61 1 1 2 23
62 1 0 3 23
62 1 1 2 23
63 1 0 3 23
63 1 1 2 23
64 1 0 3 23
64 1 1 2 23
65 1 0 3 23
65 1 1 2 23
66 1 0 3 23
66 1 1 2 23
67 1 0 3 23
67 1 1 2 23
68 1 0 3 23
68 1 1 2 23
69 1 0 3 3
69 1 1 3 3
70 1 0 3 3
70 1 1 3 3
71 1 0 3 3
71 1 1 3 3
72 1 0 3 1
72 1 1 4 1
73 1 0 4 9
73 1 1 1 9
74 1 0 4 9
74 1 1 1 9
75 1 0 4 9
75 1 1 1 9
76 1 0 4 9
76 1 1 1 9
77 1 0 4 9
77 1 1 1 9
78 1 0 4 9
78 1 1 1 9
79 1 0 4 9
79 1 1 1 9
80 1 0 4 9
80 1 1 1 9
81 1 0 4 9
81 1 1 1 9
82 1 0 4 17
82 1 1 2 17
83 1 0 4 17
83 1 1 2 17
84 1 0 4 17
84 1 1 2 17
85 1 0 4 17
85 1 1 2 17
86 1 0 4 17
86 1 1 2 17
87 1 0 4 17
87 1 1 2 17
88 1 0 4 17
88 1 1 2 17
89 1 0 4 17
89 1 1 2 17
90 1 0 4 17
90 1 1 2 17
91 1 0 4 17
91 1 1 2 17
92 1 0 4 17
92 1 1 2 17
93 1 0 4 17
93 1 1 2 17
94 1 0 4 17
94 1 1 2 17
95 1 0 4 17
95 1 1 2 17
96 1 0 4 17
96 1 1 2 17
97 1 0 4 17
97 1 1 2 17
98 1 0 4 17
98 1 1 2 17
99 1 0 4 13
99 1 1 3 13
100 1 0 4 13
100 1 1 3 13
101 1 0 4 13
101 1 1 3 13
102 1 0 4 13
102 1 1 3 13
103 1 0 4 13
103 1 1 3 13
104 1 0 4 13
104 1 1 3 13
105 1 0 4 13
105 1 1 3 13
106 1 0 4 13
106 1 1 3 13
107 1 0 4 13
107 1 1 3 13
108 1 0 4 13
108 1 1 3 13
109 1 0 4 13
109 1 1 3 13
110 1 0 4 13
110 1 1 3 13
111 1 0 4 13
111 1 1 3 13
112 1 0 4 8
112 1 1 4 8
113 1 0 4 8
113 1 1 4 8
114 1 0 4 8
114 1 1 4 8
115 1 0 4 8
115 1 1 4 8
116 1 0 4 8
116 1 1 4 8
117 1 0 4 8
117 1 1 4 8
118 1 0 4 8
118 1 1 4 8
119 1 0 4 8
119 1 1 4 8
120 0 0 1 7
120 0 1 1 7
121 0 0 1 7
121 0 1 1 7
122 0 0 1 7
122 0 1 1 7
123 0 0 1 7
123 0 1 1 7
124 0 0 1 7
124 0 1 1 7
125 0 0 1 7
125 0 1 1 7
126 0 0 1 7
126 0 1 1 7
128 0 0 1 4
128 0 1 2 4
129 0 0 1 4
129 0 1 2 4
130 0 0 1 4
130 0 1 2 4
131 0 0 1 4
131 0 1 2 4
132 0 0 1 2
132 0 1 3 2
133 0 0 1 2
133 0 1 3 2
134 0 0 1 1
134 0 1 4 1
135 0 0 2 14
135 0 1 1 14
136 0 0 2 14
136 0 1 1 14
137 0 0 2 14
137 0 1 1 14
138 0 0 2 14
138 0 1 1 14
139 0 0 2 14
139 0 1 1 14
140 0 0 2 14
140 0 1 1 14
141 0 0 2 14
141 0 1 1 14
142 0 0 2 14
142 0 1 1 14
143 0 0 2 14
143 0 1 1 14
144 0 0 2 14
144 0 1 1 14
145 0 0 2 14
145 0 1 1 14
146 0 0 2 14
146 0 1 1 14
147 0 0 2 14
147 0 1 1 14
148 0 0 2 14
148 0 1 1 14
149 0 0 2 5
149 0 1 2 5
150 0 0 2 5
150 0 1 2 5
151 0 0 2 5
151 0 1 2 5
152 0 0 2 5
152 0 1 2 5
153 0 0 2 5
153 0 1 2 5
154 0 0 2 1
154 0 1 3 1
155 0 0 3 6
155 0 1 1 6
156 0 0 3 6
156 0 1 1 6
157 0 0 3 6
157 0 1 1 6
158 0 0 3 6
158 0 1 1 6
159 0 0 3 6
159 0 1 1 6
160 0 0 3 6
160 0 1 1 6
161 0 0 3 9
161 0 1 2 9
162 0 0 3 9
162 0 1 2 9
163 0 0 3 9
163 0 1 2 9
164 0 0 3 9
164 0 1 2 9
165 0 0 3 9
165 0 1 2 9
166 0 0 3 9
166 0 1 2 9
167 0 0 3 9
167 0 1 2 9
168 0 0 3 9
168 0 1 2 9
169 0 0 3 9
169 0 1 2 9
170 0 0 3 18
170 0 1 3 18
171 0 0 3 18
171 0 1 3 18
172 0 0 3 18
172 0 1 3 18
173 0 0 3 18
173 0 1 3 18
174 0 0 3 18
174 0 1 3 18
175 0 0 3 18
175 0 1 3 18
176 0 0 3 18
176 0 1 3 18
177 0 0 3 18
177 0 1 3 18
178 0 0 3 18
178 0 1 3 18
179 0 0 3 18
179 0 1 3 18
180 0 0 3 18
180 0 1 3 18
181 0 0 3 18
181 0 1 3 18
182 0 0 3 18
182 0 1 3 18
183 0 0 3 18
183 0 1 3 18
184 0 0 3 18
184 0 1 3 18
185 0 0 3 18
185 0 1 3 18
186 0 0 3 18
186 0 1 3 18
187 0 0 3 18
187 0 1 3 18
188 0 0 3 2
188 0 1 4 2
189 0 0 3 2
189 0 1 4 2
190 0 0 4 4
190 0 1 1 4
191 0 0 4 4
191 0 1 1 4
192 0 0 4 4
192 0 1 1 4
193 0 0 4 4
193 0 1 1 4
194 0 0 4 11
194 0 1 2 11
195 0 0 4 11
195 0 1 2 11
196 0 0 4 11
196 0 1 2 11
197 0 0 4 11
197 0 1 2 11
198 0 0 4 11
198 0 1 2 11
199 0 0 4 11
199 0 1 2 11
200 0 0 4 11
200 0 1 2 11
201 0 0 4 11
201 0 1 2 11
202 0 0 4 11
202 0 1 2 11
203 0 0 4 11
203 0 1 2 11
204 0 0 4 11
204 0 1 2 11
205 0 0 4 14
205 0 1 3 14
206 0 0 4 14
206 0 1 3 14
207 0 0 4 14
207 0 1 3 14
208 0 0 4 14
208 0 1 3 14
209 0 0 4 14
209 0 1 3 14
210 0 0 4 14
210 0 1 3 14
211 0 0 4 14
211 0 1 3 14
212 0 0 4 14
212 0 1 3 14
213 0 0 4 14
213 0 1 3 14
214 0 0 4 14
214 0 1 3 14
215 0 0 4 14
215 0 1 3 14
216 0 0 4 14
216 0 1 3 14
217 0 0 4 14
217 0 1 3 14
218 0 0 4 14
218 0 1 3 14
219 0 0 4 22
219 0 1 4 22
220 0 0 4 22
220 0 1 4 22
221 0 0 4 22
221 0 1 4 22
222 0 0 4 22
222 0 1 4 22
223 0 0 4 22
223 0 1 4 22
224 0 0 4 22
224 0 1 4 22
225 0 0 4 22
225 0 1 4 22
226 0 0 4 22
226 0 1 4 22
227 0 0 4 22
227 0 1 4 22
228 0 0 4 22
228 0 1 4 22
229 0 0 4 22
229 0 1 4 22
230 0 0 4 22
230 0 1 4 22
231 0 0 4 22
231 0 1 4 22
232 0 0 4 22
232 0 1 4 22
233 0 0 4 22
233 0 1 4 22
234 0 0 4 22
234 0 1 4 22
235 0 0 4 22
235 0 1 4 22
236 0 0 4 22
236 0 1 4 22
237 0 0 4 22
237 0 1 4 22
238 0 0 4 22
238 0 1 4 22
239 0 0 4 22
239 0 1 4 22
127 0 0 4 22
127 0 1 4 22
;
proc genmod; class case;
model outcome = treat occasion treat*occasion / dist=multinomial link=clogit;
repeated subject=case / type=indep corrw;
proc nlmixed;
eta1 = alpha1 + beta1*treat + beta2*occasion + beta3*treat*occasion + u;
eta2 = alpha1 + i2 + beta1*treat + beta2*occasion + beta3*treat*occasion + u;
eta3 = alpha1 + i2 + i3 + beta1*treat + beta2*occasion + beta3*treat*occasion + u;
if (outcome=1) then p = exp(eta1)/(1 + exp(eta1));
else if (outcome=2) then p = exp(eta2)/(1 + exp(eta2)) - exp(eta1)/(1 + exp(eta1));
else if (outcome=3) then p = exp(eta3)/(1 + exp(eta3)) - exp(eta2)/(1 + exp(eta2));
else if (outcome=4) then p = 1 - exp(eta3)/(1 + exp(eta3));
ll = log(p);
model outcome ~ general(ll);
random u ~ normal(0, sigma*sigma) subject=case;
estimate 'alpha2' alpha1+i2; * this is alpha_2 in model;
estimate 'alpha3' alpha1+i2+i3; * this is alpha_3 in model;
run;
--------------------------------------------------------------------------
SAS for Model (12.3) with Matched Pairs from Table 12.1
------------------------------------------------------------------- data matched; input case occasion response count ; datalines; 1 0 1 794 1 1 1 794 2 0 1 150 2 1 0 150 3 0 0 86 3 1 1 86 4 0 0 570 4 1 0 570 ; proc nlmixed; eta = alpha + beta*occasion + u; p = exp(eta)/(1 + exp(eta)); model response ~ binary(p); random u ~ normal(0, sigma*sigma) subject = case; replicate count; -------------------------------------------------------------------
SAS for GLMM Analyses of Election Data in Table 12.2
----------------------------------------------------------------------------- data vote; input y n; case = _n_; datalines; 1 5 16 32 10 19 21 34 129 240 17 29 18 25 4 4 2 5 65 108 31 56 5 9 11 22 1 9 48 89 19 44 6 19 13 29 22 33 34 46 18 38 7 9 43 73 20 35 23 41 7 21 3 7 25 55 3 5 6 13 5 9 40 60 6 13 6 12 103 137 41 84 11 23 15 24 51 90 4 7 16 28 4 6 20 40 64 144 5 15 21 51 2 4 26 42 19 39 8 14 1 4 ; proc nlmixed; eta = alpha + u; p = exp(eta) / (1 + exp(eta)); model y ~ binomial(n,p); random u ~ normal(0,sigma*sigma) subject=case; predict p out=new; proc print data=new; -----------------------------------------------------------------------------
SAS for GLMM with Opinions on Abortion of Table 10.13
----------------------------------------------------------------------------- data new; input sex poor single any count; datalines; 1 1 1 1 342 1 1 1 0 26 1 1 0 1 11 1 1 0 0 32 1 0 1 1 6 1 0 1 0 21 1 0 0 1 19 1 0 0 0 356 2 1 1 1 440 2 1 1 0 25 2 1 0 1 14 2 1 0 0 47 2 0 1 1 14 2 0 1 0 18 2 0 0 1 22 2 0 0 0 457 ; data new; set new; sex = sex-1; case = _n_; q1=1; q2=0; resp = poor; output; q1=0; q2=1; resp = single; output; q1=0; q2=0; resp = any; output; drop poor single any; proc nlmixed qpoints = 50; parms alpha=0 beta1=.8 beta2=.3 gamma=0 sigma=8.6; eta = alpha + beta1*q1 + beta2*q2 + gamma*sex + u; p = exp(eta)/(1 + exp(eta)); model resp ~ binary(p); random u ~ normal(0,sigma*sigma) subject = case; replicate count; -----------------------------------------------------------------------------
SAS for GLMM for Leading Crowd Data of Table 12.8
------------------------------------------------------------------------------- data crowd; input mem1 att1 mem2 att2 count; datalines; 1 1 1 1 458 1 1 1 0 140 1 1 0 1 110 1 1 0 0 49 1 0 1 1 171 1 0 1 0 182 1 0 0 1 56 1 0 0 0 87 0 1 1 1 184 0 1 1 0 75 0 1 0 1 531 0 1 0 0 281 0 0 1 1 85 0 0 1 0 97 0 0 0 1 338 0 0 0 0 554 ; data new; set crowd; case=_n_; x1m=1; x1a=0; x2m=0; x2a=0; var=1; resp=mem1; output; x1m=0; x1a=1; x2m=0; x2a=0; var=0; resp=att1; output; x1m=0; x1a=0; x2m=1; x2a=0; var=1; resp=mem2; output; x1m=0; x1a=0; x2m=0; x2a=1; var=0; resp=att2; output; drop mem1 att1 mem2 att2; proc nlmixed data=new; eta=beta1m*x1m + beta1a*x1a + beta2m*x2m + beta2a*x2a + um*var + ua*(1-var); p=exp(eta)/(1+exp(eta)); model resp ~ binary(p); random um ua ~ normal([0,0],[s1*s1, cov12, s2*s2]) subject=case; replicate count; estimate 'mem change' beta2m-beta1m; estimate 'att change' beta2a-beta1a; -------------------------------------------------------------------------------
SAS for Cluster Sampling Data from Brier (1980)
-----------------------------------------------------------------------------
data new;
input nbhd satis_1 satis_2;
datalines;
1 1 1
1 2 1
1 2 1
1 2 2
1 2 2
2 1 1
2 2 1
2 2 1
2 2 2
2 2 2
3 1 2
3 1 2
3 2 2
3 2 2
3 3 2
4 1 2
4 2 1
4 2 1
4 2 2
4 3 1
5 2 2
5 2 2
5 2 2
5 2 2
5 3 2
6 1 1
6 2 1
6 2 1
6 2 1
6 2 2
7 1 1
7 1 1
7 1 1
7 2 2
7 3 2
8 1 1
8 2 1
8 2 2
8 2 2
8 2 2
9 1 1
9 1 1
9 1 1
9 3 1
9 3 3
10 1 2
10 2 2
10 2 2
10 2 2
10 2 3
11 1 1
11 1 2
11 2 2
11 2 2
11 3 1
12 1 2
12 2 1
12 2 1
12 2 1
12 2 1
13 2 1
13 2 1
13 2 1
13 2 1
13 2 2
14 2 1
14 2 2
14 2 2
14 3 3
14 3 3
15 1 1
15 1 1
15 2 1
15 2 1
15 2 2
16 1 1
16 2 1
16 2 2
17 2 1
17 2 2
17 2 3
17 3 2
17 3 2
18 2 1
18 2 3
18 3 3
19 1 1
19 1 1
19 2 1
19 2 1
19 2 2
20 1 1
20 1 1
20 2 1
20 2 1
20 3 1
;
proc logistic;
model satis_2 = satis_1;
run;
data new;
set new;
if satis_2 = 1 then do;
y1 = 1; y2 = 0; y3 = 0;
end;
else if satis_2 = 2 then do;
y1 = 0; y2 = 1; y3 = 0;
end;
else if satis_2 = 3 then do;
y1 = 0; y2 = 0; y3 = 1;
end;
else delete;
proc nlmixed data=new qpoints = 20;
*parms i1 = i2 = sigma=;
bounds i2 > 0;
eta1 = i1 + Beta * satis_1 + u;
eta2 = i1 + i2 + Beta * satis_1 + u;
p1 = 1/(1+exp(-eta1));
p2 = 1/(1+exp(-eta2)) - 1/(1+exp(-eta1));
p3 = 1 - 1/(1+exp(-eta2));
** Multinomial likelihood **;
z = (p1**y1)*(p2**y2)*(p3**y3);
** Check for small values of z **;
if (z > 1e-8) then ll = log(z);
else ll=-1e100;
** Define general log-likelihood. I think you can put any
variable **;
** before the tilde (ie. y1 or y2 or y3).
**;
model y1 ~ general(ll);
** Estimate second threshold. **;
estimate 'thresh2' i1+i2;
random u ~ normal(0,sigma*sigma) subject = nbhd;
run;
-------------------------------------------------------------------------------
SAS for Overdispersion Analyses of Table 4.5
----------------------------------------------------------------------------- data moore; input litter group n y ; z2=0; z3=0; z4=0; if group=2 then z2=1; if group=3 then z3=1; if group=4 then z4=1; datalines; 1 1 10 1 2 1 11 4 3 1 12 9 4 1 4 4 5 1 10 10 6 1 11 9 7 1 9 9 8 1 11 11 9 1 10 10 10 1 10 7 11 1 12 12 12 1 10 9 13 1 8 8 14 1 11 9 15 1 6 4 16 1 9 7 17 1 14 14 18 1 12 7 19 1 11 9 20 1 13 8 21 1 14 5 22 1 10 10 23 1 12 10 24 1 13 8 25 1 10 10 26 1 14 3 27 1 13 13 28 1 4 3 29 1 8 8 30 1 13 5 31 1 12 12 32 2 10 1 33 2 3 1 34 2 13 1 35 2 12 0 36 2 14 4 37 2 9 2 38 2 13 2 39 2 16 1 40 2 11 0 41 2 4 0 42 2 1 0 43 2 12 0 44 3 8 0 45 3 11 1 46 3 14 0 47 3 14 1 48 3 11 0 49 4 3 0 50 4 13 0 51 4 9 2 52 4 17 2 53 4 15 0 54 4 2 0 55 4 14 1 56 4 8 0 57 4 6 0 58 4 17 0 ; proc logistic; model y/n = z2 z3 z4 / scale=williams; proc logistic; model y/n = z2 z3 z4 / scale=pearson; proc nlmixed qpoints=200; eta = alpha + beta2*z2 + beta3*z3 + beta4*z4 + u ; p = exp(eta)/(1 + exp(eta)); model y ~ binomial(n,p) ; random u ~ normal(0, sigma*sigma) subject=litter; -----------------------------------------------------------------------------
SAS for Modeling Murder Data of Table 13.6
----------------------------------------------------------------------------- data new; input white black other response; datalines; 1070 119 55 0 60 16 5 1 14 12 1 2 4 7 0 3 0 3 1 4 0 2 0 5 1 0 0 6 ; data new; set new; count = white; race = 0; output; count = black; race = 1; output; drop white black other; data new2; set new; do i = 1 to count; output; end; drop i; proc genmod data=new2; model response = race / dist=negbin link=log; proc genmod data=new2; model response = race / dist=poi link=log scale=pearson; data new; set new; case = _n_; proc nlmixed data = new qpoints=400; parms alpha=-3.7 beta=1.90 sigma=1.6; eta = alpha + beta*race + u; mu = exp(eta); model response ~ poisson(mu); random u ~ normal(0, sigma*sigma) subject=case; replicate count; -----------------------------------------------------------------------------
Copyright © 2002, Alan Agresti, Department of Statistics, University of Florida.
