I have used the geeglm package to build a GEE that predicts animal activity (a binary response, active or not) from weather data (e.g., Temperature, a continuous variable).
TEMPC <- geeglm(BINARYACTIVITY ~ TEMPC, family=binomial(link="logit"), data=Animal, id=NEW_PERIOD2, corstr="ar1", std.err="san.se")
I intend for the GEE to 'account for' repeated measures; activity was assessed multiple times each hour, during sampling periods that lasted days (hence, I used "id=NEW_PERIOD2" to designate sampling periods).
I am interested in evaluating the predictive accuracy of the resulting model. Normally, for data that are not autocorrelated, I would use a logistic regression model, and assess its predictive accuracy with calculation of AUC from a ROC. However, I am uncertain how to calculate AUC for a GEE, which accounts for autocorrelation. Is there a way to calculate AUC for a GEE? If so, can I apply the method to interaction models, such as the one I pasted below?
TEMPC_COSHOUR <- geeglm(BINARYACTIVITY ~ TEMPC*COSHOUR, family=binomial(link="logit"), data=Animal, id=NEW_PERIOD2, corstr="ar1", std.err="san.se") #COSHOUR is a a transformed Time variable
Thank you for any advice you can provide!