*options obs=0;
options obs=max replace fmterr=off;
options ps=67 ls=120;

libname mlm 'c:\Documents and Settings\sdneuper\My Documents\data';

data new; set mlm.family;
run;

proc contents data = new;
run;


*fully unconditinal (null) model;
proc mixed data=merged noclprint covtest;
class caseid;
model dbadmood= 
 /solution ddfm=bw;
   random intercept  /subject=caseid ;
run;


*grand-mean centering;
proc means data = name;
var item1;
run;

data name; set name;
cvar = raw – mean;
run;


*group-mean centering;
proc summary data =daily;
class caseid;
var  dbadmood physum;
output out=out2
(keep= caseid dbadmood physum)
mean=dbadmood physum;
run;

data out3; set out2;
if caseid=. then delete;
aphys=physum;
anegmood=dbadmood;
run;

data out3; set out3 (drop= physum dbadmood);
run;

proc sort data = out3;
by caseid;
run;

proc sort data = daily;
by caseid;
run;

data both;
merge out3 daily;
by caseid;
run;


*oneway ANCOVA with random effects;
proc mixed data=merged noclprint covtest;
class caseid;
model dbadmood= day
   /solution ddfm=bw;
   random intercept  /subject=caseid ;
run;


*random coefficients regression;
proc mixed data=merged noclprint covtest;
 class caseid;
 model dbadmood= day
  /solution ddfm=bw;
random intercept day /subject=caseid type = un ;
run;

*means-as-outcomes regression;
proc mixed data=merged noclprint covtest;
 class idno;
 model satis= size
   /solution ddfm=bw;
   random intercept  /subject=idno ;
run;

*intercepts and slopes as outcomes;
proc mixed data=merged noclprint covtest;
 class idno;
 model satis= neurot size neurot*size
   /solution ddfm=bw;
   random intercept neurot /subject=idno type = un ;
run;

*non-randomly varying slopes;
proc mixed data=merged noclprint covtest;
class idno;
 model satis= neurot size neurot*size
   /solution ddfm=bw;
   random intercept  /subject=idno type = un ;
run;