I have a logistic model fitted with the following R function:
glmfit<-glm(formula, data, family=binomial)
A reasonable cutoff value in order to get a good data classification (or confusion matrix) with the fitted model is 0.2 instead of the mostly used 0.5.
And I want to use the
cv.glm function with the fitted model:
cv.glm(data, glmfit, cost, K)
Since the response in the fitted model is a binary variable an appropriate cost function is (obtained from "Examples" section of ?cv.glm):
cost <- function(r, pi = 0) mean(abs(r-pi) > 0.5)
As I have a cutoff value of 0.2, can I apply this standard cost function or should I define a different one and how?
Thank you very much in advance.