Proc Logistic Without specification SAS models the first

Proc Logistic

Without specification, SAS models the first value of dependent variable as the “event”, in this case, y=0. proc logistic data= a. chd 2018_a plots=none; model chd=age; run;

Without specification, SAS models the first value of dependent variable as the “event”, in this case, y=“Developed Chd”. proc freq data=s 5238. chd 2018; tables chd*age; run; proc logistic data= s 5238. chd 2018 plots=none; model chd=age; run;

Explicitly specify event value proc logistic data=a. chd 2018_a plots=none; model chd(event="1")=age; run;

Explicitly specify event value proc logistic data= s 5238. chd 2018 plots=none; model chd(event="Developed Chd")=age; run;

The descending option, in this case, the second value is modeled (y=1). proc logistic data=a. chd 2018_a plots=none descending; model chd=age; run;

Scoring new data to_predict; do age=25 to 50; output; end; run; proc logistic data=a. chd 2018_a noprint; model chd(event="1")=age; score data=to_predict out=tmplog; run; proc print data=tmplog; run;

Scoring the data set used – the out= option proc logistic data=a. chd 2018_a noprint; model chd(event="1")=age; score out=tmplog; run; proc print data=tmplog; run;

Create a data set with estimated coefficients proc logistic data=a. chd 2018_a outest=betas noprint; model chd(event="1")=age; run; proc print data=betas; run;

Create a file of estimated parameters using ODS. ods output parameterestimates=betas; ods select parameterestimates; proc logistic data=a. chd 2018_a; model chd(event="1")=age; run; proc print data=betas; run;

Create a file of estimated parameters using ODS. By group processing. proc sort data=a. chd 2018_a out=tmp; by male; run; ods output parameterestimates=betas; ods select parameterestimates; proc logistic data=tmp; by male; model chd(event="1")=age; run; proc print data=betas; run;

Add covariance matrix to output. proc logistic data=a. chd 2018_a outest=betas covout; model chd(event="1")=age; run; proc print data=betas; format age 12. 10; run;

Display covariance matrix. proc logistic data=a. chd 2018_a; model chd(event="1")=age/covb; run;

Examine ODS output files. ods trace on; proc logistic data=a. chd 2018_a; model chd(event="1")=age; run; ods trace off;

Put fit statistics into a file ods output fitstatistics=likelihood; ods select fitstatistics; proc logistic data=a. chd 2018_a; model chd(event="1")=age; run; proc print data=likelihood; run;

Specify plots. proc logistic data=a. chd 2018_a plots=effect; model chd(event="Developed Chd")=age; run;

The freq statement proc freq data=a. chd 2018_a noprint; tables age*chd/out=chdagecnt; run; proc print data=chdagecnt; run; proc logistic data=chdagecnt; model chd(event="Developed Chd")=age; freq count; run;

Create a binomial version of the data. Each observation is the number of observations and the number of events. proc sql; create table positives as select age, count(*) as n 1 from a. chd 2018_a where chd= 1 group by age ; create table counts as select age, count(*) as n from a. chd 2018_a group by age ; create table binary as select a. age, n 1, n from positives a, counts b where a. age=b. age ; select * from binary; quit;

ods select parameterestimates; proc logistic data=a. chd 2018_a; model chd(event="1")=age; run; ods select parameterestimates; proc logistic data=binary; model n 1/n=age; run;