[R] Prediction Error Calculation

[quaildoc]

Hello List,

I am fitting a logistic regression model for some presence/absence type
data.  I have numerous covariates I am fitting to explain variation, and I
am using AIC to rank models.  However, I would like to report how well my
best model (s) do at prediction.  I have looked over the archives and the
web and have come up with something that gives me what I think is the mean
prediction error, BUT I am not sure of that. I am sort of unfamiliar with
these types of statistics.  Here is my code:


metrics.global<-glm(Type~MPI+IJI+ED+PRD+class2+class3+class5,
family=binomial, data=metrics)## ##Type is my binary response 0 or 1

muhat<-metrics.global$fitted.values
##assigns the fitted values a name muhat
global.diag<-glm.diag(metrics.global)
##creates a the diagnostic values
cv.err<-mean((metrics.global$y-muhat)^2/(1-global.diag$h)^2)
###calculates cv.err
cv.err


My main problem is I am unsure how to interpret what cv.err means for my
model.  I know that h is a leverage statistic for each observation.  I would
appreciate some interpretation clarification.

Thank you.




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