proc FREQ (in SAS and Excel)

SAS

data xxx;

input   int $   trt  $ no;

datalines;

 mild  R 13

 mode R 0

 sev R 2 

  mild T 13

 mode T 3

 sev T 0

 ;

 run;

 proc freq data = xxx ;

 weight no ;

 tables  trt *int /chisq ;

 run ;

data respire; input treat $ outcome $ count ; 

cards; 

test f 40 

test u 20 

placebo f 16 

placebo u 48; 

proc freq; 

weight count; 

tables treat*outcome/measures chisq; 

run;

 참고파일

Sas_web.pdf

#SAS Code for Confidence Intervals for a Proportion

data veg; 

  input response $ count; datalines; 

       no 25 yes 0 ; 

run ;

proc freq data=veg; 

  weight count; tables response / binomial(ac wilson exact jeffreys) alpha=.05; 

run;

#SAS Code for Chi-Squared, Measures of Association, and Residuals for Data on Education

data table; 

  input degree belief $ count @@; 

  datalines; 1 1 9 1 2 8 1 3 27 1 4 8 1 5 47 1 6 236 2 1 23 2 2 39 2 3 88 2 4 49 2 5 179 2 6 706 3 1 28 3 2 48 3 3 89 3 4 19 3 5 104 3 6 293 ; 

run ;

proc freq order=data; 

 weight count; tables degree*belief / chisq expected measures cmh1; 

run ;

proc genmod order=data; class degree belief; model count = degree belief / dist=poi link=log residuals; 

run;

 

#: SAS Code for Confidence Intervals for 2×2 Table

data example;

 input group $ outcome $ count @@; 

datalines; placebo yes 2 placebo no 18 active yes 7 active no 13 ; 

proc freq order=data; weight count; 

   tables group*outcome / riskdiff(CL=(WALD MN)) measures; 

       * MN = Miettinen and Nurminen inverted score test; 

run; 

#SAS Code for Fisher’s Exact Test and Confidence Intervals for Odds Ratio

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; 

run;

# SAS Code for “Exact” Confidence Intervals for 2×2 Table

data example; 

input group $ outcome $ count @@; 

datalines; placebo yes 2 placebo no 18 active yes 7 active no 13 ; 

proc freq order=data; weight count; 

tables group*outcome ; 

exact or riskdiff(CL=(MN)) ; 

run; 

proc freq order=data; weight count; 

tables group*outcome ; 

exact riskdiff(method=score); 

* exact unconditional inverting two one-sided score tests; 

run; 

#SAS Code for Binary GLMs for Snoring Data

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 LRCI; proc genmod; model disease/total = snoring / dist=bin link=probit; run;