GLIMMIX 0 Standard Error Infinity t value

[Nerdcy]

Yes, after fixing the issues of ID and CLASS I'm still running into the same issues. I have attached my residuals (which actually look the same whether I use the logged predictors or not).

[sld]

At this point, I don't know what model you are now running or what "the same issues" are. Presumably the model with log transformed predictors (all predictors? all predictors except time? transformed and then centered? transformed and then standardized?) appears to run without apparent errors? If you would like more input, provide us with an update of your model and your output.

[Nerdcy]

Sorry for the confusion. 

The model with the logged continuous predictors (not including time) works fine. I guess my question is more statistical in nature. My professor didn't want me to standardize these variables (which also worked) because it makes the random effects difficult to interpret. I guess my question is if I log the predictors, would that also make the random effects difficult to interpret or would I just follow the usual rules for interpreting after a log transformation?

[sld]

I am puzzled by many aspects of your response, and I wonder if you meant to write about interpreting fixed effects rather than random effects.

With a model such as yours, I would say that we generally are not interested in interpreting the random effects, other than evaluating (either formally or informally) whether the variances/covariances are different than zero.

The scale of the random effects is determined by the scale of the response, not the scales of the predictors. With a gamma distribution and a log link, (co)variances are on the log scale.

We are, of course, interested in interpreting the fixed effects, and then the scales of both response and predictors do matter. Transformations and/or links (here, log for the gamma) change the fundamental form of the relationship between response and predictor; if you are fitting a linear regression model (including a generalized linear mixed model), then some combinations of scales for response and predictors may better meet the assumptions of linearity. Rescaling, such as centering or standardizing, does not affect the form of the relationship.

As personal rules of thumb: Ideally, I would only transform predictors to better meet linearity assumptions, or for some other "sensible" reason. For example, if one of my predictors is a pre-treatment/baseline measure of the same variable as the response, I transform the predictor to match the scale of the response. (I do not think you have a baseline predictor here.)

As for rescaling (as distinct from transformation), I generally center continuous predictors in regression, and I always center continuous predictors in regression when the predictors are involved in interaction. If I am having estimation trouble, or if I want to interpret predictor effects relative to a one standard deviation change, I standardize continuous predictors. Rescaling does not present any complicated challenges to interpretation.

I hope this helps move you forward.

If you have a professor, then you are at an institution where you might be able to find a statistician to provide more intensive help.

[SteveDenham]

That Q-Q plot tells me that the link you have (or the distribution) isn't quite appropriate, as the residuals get much larger than expected as you move to higher quantiles. You may need to do something about the skewness of the residuals.

SteveDenham

[Nerdcy]

It was my understanding that the QQ plot was for a normal distribution but I am using a Gamma distribution to account for the positive skew. Is this incorrect? Does 

plots=residualpanel

account of the Gamma distribution already? 

[SteveDenham]

I think that is correct - the QQ plot of the residuals in GLIMMIX assumes a normal distribution for the quantiles, so it will bend high. You can't really fit residuals to a gamma, as there are negative values, and log(negative of anything) is undefined.

SteveDenham