data crab;
input color spine width satell weight;
if satell>0 then y=1;
if satell=0 then y=0;
n=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
;
run;
proc print data=crab;
run;
data crab1;
set crab;
wcat=0;
if width<=23.25 then wcat=1;
if 23.25 < width <=24.25 then wcat=2;
if 24.25 < width <=25.25 then wcat=3;
if 25.25 < width <=26.25 then wcat=4;
if 26.25 < width <=27.25 then wcat=5;
if 27.25 < width <=28.25 then wcat=6;
if 28.25 <width <=29.25 then wcat=7;
if width > 29.25 then wcat=8;
run;
proc sql;
create table midpt as
select*, mean(satell) as smean, mean(width)
as wmean, std(satell)*std(satell) as varsatell,
count(wcat) as total, sum(y) as yes
from crab1
group by wcat;
quit;
proc sort data=midpt;
by wcat;
run;
data midpt2;
set midpt;
by wcat;
if first.wcat;
run;
data midpt2;
set midpt2;
proportion=yes/total;
run;
proc print data=midpt2;
run;
proc genmod data=midpt2;
model yes/ total=wcat/dist=bin link=logit;
output out=temp1 p=pred1;
run;
/* this model is used for ungrouped data*/
/* default is the wald CI*/
proc genmod descending data=crab1;
model y=width/dist=bin link=logit;
run;
proc genmod descending data=crab1;
model y=wcat/dist=bin link=logit;
run;
proc genmod data=crab1;
model y=wcat/dist=bin link=logit lrci;/* likelihood ratio CI*/
run;
proc genmod descending data=crab1;
model y=width/dist=bin link=logit lrci/* likelihood ratio CI*/
covb; /* covariance of alpha and beta */
output out=temp p=pred upper=ucl
lower=lcl;
run;
data aids;
input yes no azt$ race$ ;
total=yes+no;
datalines;
14 93 yes white
32 81 no white
11 52 yes black
12 43 no black
;
run;
proc print data=aids;
run;
proc genmod data=aids; class race azt;
model yes/total=azt race/dist=bin link=logit;
run;
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