01-05-2017 04:37 PM - edited 01-05-2017 05:56 PM
I am working on a project involving understanding interday and intraday varainces on a device and I found interday varaince to be less than intraday, which is our biggest concern.
So we took measurements on humans on 5 days, and 5 masurements on each day and each measurement gives us 12 outputs of the same measure at any given time. So I created a completely nested random effect model as follows to get all varaince estimates :
proc mixed data=required covtest cl;
class pt day scan ;
model var= ;
random pt day(pt) scan(day*pt) ;
estimate "Mean" intercept 1/cl;
ods output covparms=Covparms estimates=EstimatedMean;
To investigate why intraday varaince is higher than interday, I want to test if subgroups (say Male and female) have equal interday and intarday varaince or how their variances are. Now I am not quite sure how can I get varaince estimates in these subgroups. Any help is greatly appreciated. Any other solution/trick to investigate is welcome too.
Thanks soooo much for any help!!
01-16-2017 02:40 PM
Why not introduce the subgrouping as a fixed effect, and include it in a group= option to the random statement. For example, if sex were the factor, you might try:
proc mixed data=required covtest cl; class pt day scan sex; model var= sex; random pt day(pt) scan(day*pt)/group=sex ; estimate "Mean" intercept 1/cl; ods output covparms=Covparms estimates=EstimatedMean; run;