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
