This output shows that the results for only 32 study subjects and only 855 out of 1,455 observations were used in the PROC MIXED analysis. This loss seems somewhat excessive. In previous abbreviated lists of data on this Internet site, a few variables had missing values. Do you think that this is why so many observations were excluded from the analysis? It may be worthwhile to check the pattern of missing dependent variables and independent variables to determine whether you can exclude some variables with large percentages of missing values from the analysis. For example, you can display the pattern of missing values by using the procedure, PROC MI, as follows: proc mi nimpute=0; var y var task height weight exerc1-exerc20; run; quit; I followed the PROC MIXED documentation for the MODEL statement syntax including the VAR*TASK interaction as an independent variable (see the documentation for the REPEATED statement option, TYPE, for multivariate repeated measures. However, if the model that includes this VAR*TASK interaction does NOT converge, and if the model that excludes this interaction DOES converge, then the latter model may be the way to go. Another alternative model would be to use the MODEL statement that includes only the VAR*TASK interaction without including the main-effect terms, VAR or TASK, as independent variables (the "cell means" model): model response=var*task height weight . . . .; I prefer to use the condition number rather than the VIF statistic in PROC REG to identify groups of highly correlated independent variables. A condition number of 30 or more (10 or more in models without intercept terms) and independent variables with relatively large variance proportions will identify such group(s). Then, from subject matter knowledge or from the use of PROC VARCLUS to group highly correlated variables, you can select one or a few variables within each group to represent all the highly correlated variables in the group for use as independent variables in subsequent modelling. Since only one of the 20 exercises (exerc7) remained statistically significantly associated with the response after adjusting for VAR and TASK, this specific exercise may or may not be substantively associated with the response (perhaps a multiple comparison problem); perhaps subject-matter knowledge about what this exercise represents will help you to decide whether to keep it in the model or not. However, to decide whether or not to include it in your assessment of the value of these exercises in predicting the response by comparing the information criteria of different models, I would compare the model having only VAR, TASK, WEIGHT, and HEIGHT as independent variables with the model having VAR, TASK, WEIGHT, HEIGHT, and all 20 of the exercise variables (not just EXERC7) because you didn't have any a priori reason to select only EXERC7.
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