Hi, my name is Andy and I'm analyzing a large dataset using SAS Proc Glimmix
procedure. My dataset contains over 20,000 GPS records. I'm trying to
evaluate why certain deer were observed during hunting season thus I've coded
the deer that were observed with a "1" and those not observed with a "0." I
coded the entire our that the deer was observed to encompass any hunter
recording errors. My model is shown below:
PROC GLIMMIX DATA=OBS METHOD=LAPLACE;
CLASS ID YEAR EXPOSURE HABITAT_VALUE;
MODEL OBSERVED (EVENT = '1') = EXPOSURE STEPLENGTH HABITAT_VALUE ELEVATION
DIST_NEAREST_ROAD / DIST=BINARY LINK=LOGIT SOLUTION;
RANDOM ID YEAR;
RUN;
I want to see if the different independent variables influence the
observation of deer throughout the hunting season. My question is what are
the assumptions that I need to adhere to with logistic regression. I read
that the data does not need to be normally distributed. I know "steplength"
is extremely right skewed with the mean of 48 meters and a max value of 1,400
meters. If normality is not an issue then I assumed the next step would be to
at least examine the residuals and remove some of those extreme movements. I
added the PLOT=RESIDUALPANEL option to my model with ODS GRAPHICS and plotted
the residuals. The residuals looked very different than what I'd see in a
PROC MIXED model and I was unable to interpret the plots to determine if I
need to remove any outliers. Will I not receive a normal residual plot,
similar to PROC MIXED? If so, how do you interpret residual plots from PROC
GLIMMIX. Thank you very much!
