Hi all,

I'd like to ask some question on my model run by R and SAS.

Dust were measured during 15 years,for 9 companies.
The companies have 11 jobs, so job are nested within company.
The data of measurements was severly unbalanced by years and jobs.

First, I run the random intercept model like belows, and the results were
exactly same.


R<-lme(fixed=ltwa~1, data=dust, method="REML", random=~1|company/job))

*proc* *mixed* data=dust covtest;

class job company ;

model ltwa=/solution covb;

random intercept/subject=company solution;

random intercept/subject=job(company)solution;

*run*;

Second, I wanted to treat year as a random effect (random intercept, random
slop model),

so I added year in the model, but the results were different in R and SAS.

R<-lme(fixed=ltwa~year, data=dust, method="REML", random=~year|company/job))

*proc* *mixed* data=dust covtest;

class job company ;

model ltwa=year/solution covb;

random intercept year/subject=company solution;

random intercept year/subject=job(company)solution;

*run*;

I can't figure out why the results were different.

Could you give a advice how to correct or improve my models?

Thank you in advance.



Dong-Hee Koh.DrPH.

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