> I was hoping to get positive values with the code below but I ended up having missing and negative values.
The code will give you only nonnegative values for Y. If NetNit is missing, then Y will be missing (as it should). The code translates the data by some data-dependent value into the interval [1, infinity).
The interpretation is that you are measuring the data using some different baseline reference. For example, if you measure elevation from sea level, the elevations of Death Valley and the Mariana Trench are negative. But if you measure from one meter below the bottom of the Mariana Trench, then all elevations on earth are positive. In the language of distributions, you are translating the data distribution so that it has a new mean, but preserving other properties of the distribution.
I don't know your model, so I can't really comment on PROC GLMMIX. But I encourage you to consider whether you should log-transform negative values in the first place.
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