library(MASS)

model1 <- glm(type~.,family=binomial(link="logit"),data=Pima.tr)
proba.logit <- predict(model1,Pima.te,type="response")

classif.logit <- proba.logit>=1/2
classif.logit[classif.logit==TRUE] <- "Yes"
classif.logit[classif.logit==FALSE] <- "No"
mean(classif.logit!=Pima.te$type)

model2 <- glm(type~.,family=binomial(link="probit"),data=Pima.tr)
proba.probit <- predict(model2,Pima.te,type="response")

classif.probit <- proba.probit >=1/2
classif.probit[classif.probit==TRUE] <- "Yes"
classif.probit[classif.probit==FALSE] <- "No"
mean(classif.probit!=Pima.te$type)

curve(plogis,-10,10)
curve(pnorm,-10,10,col="red",add=TRUE)