I have a basic question regarding modelling random effects in Glimmix.
I have a binary dependent variable.
I have fecal bacterial isolates from 2 cohorts of animals nested within farms, I have multiple samples from animals within the same sampling time, but also over two week intervals.
I have weekly samples from animals. Every time I sampled the animals I have data on 3 isolates per animal and sampling time. I have two cohorts of animals that I followed with approx 6 months apart. I have over 30 farms in my database.
I want to control for the fact that I repeatedly sampled the same animals, but also that I had clustering of animals within a cohort and within a farm.
Since bacterial isolates in these animals are very transitory, it is not really a 'repeated measure' on the animals.
I was thinking something like this below, but am new to glimmix. It seem to have so many random options, I just want to control for the clustering of isolates within animals within cohorts and farms.
Proc glimmix data=;
class animal cohort farm x y:
model y= x/dis=bin link=logit oddsration;
random intercept/subjet=farm;
random intercept/subject=animal(farm) type=un;
run;
Cat.
This looks good--some minor typos (oddsration should be oddsratio, subjet should be subject). I would change the type=un to type=chol, and would add method=quad or method=laplace to the proc statement. To get:
Proc glimmix method=quad data=
class animal cohort farm x y:
model y= x/dis=bin link=logit oddsratio;
random intercept/subject=farm;
random intercept/subject=animal(farm) type=un;
run;
I hope this helps.
Steve Denham
Don't miss out on SAS Innovate - Register now for the FREE Livestream!
Can't make it to Vegas? No problem! Watch our general sessions LIVE or on-demand starting April 17th. Hear from SAS execs, best-selling author Adam Grant, Hot Ones host Sean Evans, top tech journalist Kara Swisher, AI expert Cassie Kozyrkov, and the mind-blowing dance crew iLuminate! Plus, get access to over 20 breakout sessions.
Learn the difference between classical and Bayesian statistical approaches and see a few PROC examples to perform Bayesian analysis in this video.
Find more tutorials on the SAS Users YouTube channel.