I'm assuming that SMC is squared multiple correlation. Could you share more about your model? It seems that you may have some negative variance terms in your results that you should investigate before interpreting results.

Depends on your model. In many models, squared multiple correlations may be defined in terms of variance reduction for a given variable, with the squared multiple correlation defined as total variance (from either a saturated model or from the model expected variance) minus the residual variance for any variable. There are two things to note here. First, this squared multiple correlation is model dependent; not only is the SMC in this case tied to all predictor variables, constraints and other aspects of the model can affect the appropriate model parameters and thus the SMC. Second, this definition breaks when the variable of interest predicts other variables or is in a non-recursive model, as an estimated residual variance term is an incomplete estimate of the variance unique to any variable in this case.

If you provide code and more information about what you're trying to do, we can be of more help.

I'm assuming that SMC is squared multiple correlation. Could you share more about your model? It seems that you may have some negative variance terms in your results that you should investigate before interpreting results.

I tried to obtain SMC (squared multiple correlations) for hours. Could you give me a hint how to calculate and output it?

Thanks in advance

Thomas

Depends on your model. In many models, squared multiple correlations may be defined in terms of variance reduction for a given variable, with the squared multiple correlation defined as total variance (from either a saturated model or from the model expected variance) minus the residual variance for any variable. There are two things to note here. First, this squared multiple correlation is model dependent; not only is the SMC in this case tied to all predictor variables, constraints and other aspects of the model can affect the appropriate model parameters and thus the SMC. Second, this definition breaks when the variable of interest predicts other variables or is in a non-recursive model, as an estimated residual variance term is an incomplete estimate of the variance unique to any variable in this case.

If you provide code and more information about what you're trying to do, we can be of more help.