I have individuals on which we measured 4 dependent variables: 2 are normal and 2 are negative binomial. I would like to fit a multivariate model in order to gain more power in the analysis. The dataset has one column for the responses (COL1) and one column for the type of sitribution (dist). I looked at the example in the SAS documentation, so I wrote: proc glimmix data=project.tall;
class dist tieger_id;
model COL1 = dist dist*FFWeekc / noint s dist=byobs(dist);
random intercept / subject=tieger_id;
run; but I get the following error: ERROR: Use the GROUP=DIST option in the RANDOM _RESIDUAL_ statement to accommodate the scale parameter from each distribution that you specify in the BYOBS=DIST option. So I tried: proc glimmix data=project.tall;
class dist tieger_id;
model COL1 = dist dist*FFWeekc / s dist=byobs(dist);
random _residual_ / subject=tieger_id type=chol;
run; WARNING: The R matrix depends on observation order within subjects. Omitting observations from the analysis because of missing values can affect this matrix. Consider using a classification effect in the RANDOM _RESIDUAL_ statement to determine ordering in the R matrix. ERROR: Use the GROUP=DIST option in the RANDOM _RESIDUAL_ statement to accommodate the scale parameter from each distribution that you specify in the BYOBS=DIST option. and finally: proc glimmix data=project.tall;
class dist tieger_id;
model COL1 = dist dist*FFWeekc / s dist=byobs(dist);
random _residual_ / subject=tieger_id type=chol group=dist;
run; and I get the same error message. Without random statement I get the results, but since I did not take into account the correlation between the response for each individual, does it correspond to a multiavriate model? And in case I do not use the "dist" in the model what would be the interpretation? Thank you very much
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