06-28-2014 05:39 PM
I am using proc mixed model, and i got the following warning.
WARNING: Stopped because of infinite likelihood.
WARNING: Output 'lsmeans' was not created. Make sure that the output object name, label, or path
is spelled correctly. Also, verify that the appropriate procedure options are used to
produce the requested output object. For example, verify that the NOPRINT option is not
The model is working fine with individual visit data but when I run the model with all the visits it failed.
No duplicate records, no missing data. The same model worked for even lesser population.
proc mixed data=dsnin;
class treatment visit subjid ;
model pchg = treatment visit treatment *visit base/solution ddfm=satterthwaite;
repeated visit/subject=subjid type=un;
lsmeans treatment *visit/cl alpha=0.05;
ods output lsmeans=means;
Any kind of help is greatly appreciated.
06-30-2014 09:50 AM
Almost certainly the infinte likelihood is due to duplicate records. For each subjid, there can be only one record per "visit". If the subjid is not unique within treatment, then this error will occur. If that is the case, then change subject=subjid to subject=subjid*treatment, and all should be well. Otherwise, you are going to have clean your data so that there are no duplicate records for visit.
06-30-2014 10:55 PM
I have a question about error source in split-split plot design. I have combined two years of data as follows;
class Year Rep Irrig Tillage Variety;
model HJul24 = Irrig|Tillage|Variety;
Random Rep(Year) Rep(Year Irrig) Rep(Year Irrig Tillage);
Irrig was the main plot, Tillage was the subplot, and Variety was the sub-subplot in three replicates. I would like to consider year in the random model. My question is; should I include the Tillage in the random model or not. As I will say in the paper;
Irrigation, tillage, variety, and their interaction were considered as fixed effects. Year, replicate, and their interaction with irrigation and tillage were considered as random effects.
Thank you very much.
07-01-2014 07:27 AM
If you have sufficient data to estimate the variance components with tillage included, I would put them into the random statement. However, I would be very surprised if the message "G matrix is not postive definite" or some such does not appear. The reason for still including them is that they are part of the design, and the degrees of freedom for tests should reflect the "skeleton ANOVA" (see Stroup's Generalized Linear Mixed Models for more on this).