*** SAS file generated by sastangle ***;

options nocenter nodate nonumber ls=80 formdlim='';

*** Code chunk number 1 ***;
data test;
do cat = "blue", "red";
  do i = 1 to 50;
    x = normal(1966);
	output;
	end;
  end;
run;

*** Code chunk number 2 ***;
proc summary data=test;
   output out=whole mean=meanx;
   var x;
run;

proc print data=whole;
run;

*** Code chunk number 3 ***;
proc sort data=test; 
   by cat;
run;

proc summary data=test;
   by cat;
   output out=newds mean=meanx;
   var x;
run;

proc print data=newds;
run;

*** Code chunk number 4 ***;
proc sort data="c:/book/help.sas7bdat";
   by female;
run;

*** Code chunk number 5 ***;
ods select none;
ods output parameterestimates=params;
proc reg data="c:/book/help.sas7bdat";
   by female;
   model i1 = age;
run;
ods select all;

*** Code chunk number 6 ***;
options ls=64;
proc print data=params; run;

*** Code chunk number 7 ***;
data simpower1;
   effect = 0.35;  /* effect size */
   corr = 0.4;     /* desired correlation */
   covar = (corr)/(1 - corr);  /* implied covariance given variance = 1*/
   numsim = 1000;  /* number of datasets to simulate */
   numfams = 100;  /* number of families in each dataset */
   numkids = 3;    /* each family */
   do simnum = 1 to numsim;   /* make a new dataset for each simnum */
      do famid = 1 to numfams; /* make numfams families in each dataset */
         inducecorr = normal(42)* sqrt(covar);
	                     /* this is achieves the desired
	                        correlation between kids within family */
         do kidnum = 1 to numkids;   /* generate each kid */
            exposed = ((kidnum eq 1) or (famid le numfams/2)) ;
	                              /* assign kid to be exposed */
            x = (exposed * effect) +
                (inducecorr + normal(0))/sqrt(1 + covar);
            output;
         end;
      end;
   end;
run;

*** Code chunk number 8 ***;
ods select none;
ods output solutionf=simres;
proc mixed data=simpower1 order=data;
by simnum;
   class exposed famid;
   model x = exposed / solution;
   random int / subject=famid;
run;
ods select all;

*** Code chunk number 9 ***;
data powerout;
set simres;
   where exposed eq 1;
   reject=(probt lt 0.05);
run;

*** Code chunk number 10 ***;
proc freq data=powerout;
   tables reject / binomial (level='1');
run;

*** Code chunk number 11 ***;
options ls=64;

/* make the data */
data testmcmc;
   x=0; count=0; n=100; output;
   x=1; count=5; n=100; output;
run;

ods select postsummaries;
proc genmod descending data=testmcmc;
   model count/n = x / dist=binomial link=logit;
   bayes seed=12182002 nbi=100 nmc=2000
   coeffprior=normal(var=25)
   statistics(percent=2.5,50,97.5)=summary;
run;
ods select all;

*** Code chunk number 12 ***;
proc import datafile='c:/book/help.csv'
   out=help dbms=dlm;
   delimiter=',';
   getnames=yes;
run;

*** Code chunk number 13 ***;
ods select postintervals;
proc genmod data=help;
   class substance (param=reference ref='alcohol');
   model i1 = female substance age / dist=poisson;
   bayes nbi=100 nmc=2000;
run;
ods select all;

*** Code chunk number 14 ***;
ods select parameterestimates;

proc glm data="C:/book/help.sas7bdat" order=data;
   class homeless;
   model pcs = homeless / solution;
run; quit;

ods select all;

*** Code chunk number 15 ***;
ods select parameterestimates;

proc glm data="C:/book/help.sas7bdat" order=data;
   class homeless female;
   model pcs = homeless age female i1 mcs / solution;
run;

ods select all;

*** Code chunk number 16 ***;
ods select none;

proc logistic data="C:/book/help.sas7bdat" order=data;
   class homeless female;
   model homeless(event='1') = age female i1 mcs;
   output out = propen pred=propensity;
run;

ods select all;

*** Code chunk number 17 ***;
ods select parameterestimates;

proc glm data=propen order=data;
   class homeless;
   model pcs = homeless propensity/ solution;
run; quit;

ods select all;

*** Code chunk number 19 ***;
ods select parameterestimates;

proc glm data="c:\book\mpropen.sas7bdat" order=data;
   where matched eq 1;
   class homeless;
   model pcs = homeless / solution;
run;

ods select all;

*** Code chunk number 20 ***;
proc means data=propen mean std;
   class homeless;
   var age pcs;
run;

*** Code chunk number 21 ***;
proc means data="c:/book/mpropen.sas7bdat" mean std;
   where matched;
   class homeless;
   var age pcs;
run;

*** Code chunk number 25 ***;
options ls=64;
proc print data="c:/book/bootciout.sas7bdat" label; run;

*** Code chunk number 26 ***;
data missdemo;
   missing = (x1 eq .);
   x2 = (1 + 2 + .)/3;
   x3 = mean(1, 2, .);

   if x4 = 999 then x4 = .;

   x5 = n(1, 2, 49, 123, .);
   x6 = nmiss(x2, x3);

   x7 = 1;
   if x7 ne .;
   if x7 ne . then output;
run;

*** Code chunk number 27 ***;
proc print data=missdemo;
run;

*** Code chunk number 28 ***;
filename localf "c:/book/helpmiss.csv" lrecl=704;

proc import replace datafile=localf out=help dbms=dlm;
   delimiter=',';
   getnames=yes;
run;

*** Code chunk number 29 ***;
options ls=64;
ods select misspattern;
proc mi data=help nimpute=0;
   var homeless female i1 sexrisk indtot mcs pcs;
run;
ods select all;

*** Code chunk number 30 ***;
proc mi data=help nimpute=20 out=helpmi20 noprint;
   class homeless female;
   fcs logistic regpmm;
   var homeless female i1 sexrisk indtot mcs pcs;
run;

*** Code chunk number 31 ***;
ods select none;
ods output parameterestimates=helpmipe covb=helpmicovb;
proc logistic data=helpmi20 descending;
by _imputation_;
   model homeless(event='1')=female i1 sexrisk indtot / covb;
run;
ods select all;

*** Code chunk number 32 ***;
ods select varianceinfo;
proc mianalyze parms=helpmipe covb=helpmicovb;
   modeleffects intercept female i1 sexrisk indtot;
run;

*** Code chunk number 33 ***;
options ls = 68;
ods select parameterestimates;
proc mianalyze parms=helpmipe covb=helpmicovb;
   modeleffects intercept female i1 sexrisk indtot;
run;
ods select all;

*** Code chunk number 34 ***;
ods select parameterestimates;
proc logistic data=help;
   model homeless(event='1')=female i1 sexrisk indtot;
run;
ods select all;

*** Code chunk number 35 ***;
data fmmtest;
do i = 1 to 5000;
  x = normal(1492);
  group = (exp(-1 + 2*x)/(1 + exp(-1 + 2*x))) gt uniform(0);
  y = (group * 3) + ((1 + group/5) * x) +  normal(0) * sqrt(1);
  output;
  end;
run;

*** Code chunk number 37 ***;
ods select parameterestimates;
proc fmm data=fmmtest;
   model y = x / k=2 equate=scale;
   probmodel x;
run;

*** Code chunk number 38 ***;
ods select mixingprobs;
proc fmm data=fmmtest;
   model y = x / k=2 equate=scale;
   probmodel x;
run;