R's document says that delta is the raw cross-validation estimate of prediction error, which i think is prediction error rate in the situation of logistic regression. However, when i try to calculate prediction error rate with my own function the result is different.

cv.glm:

    > fit=glm(Direction~Lag1+Lag2,family = binomial,data = Weekly)
    > cv.err=cv.glm(Weekly,fit)
    > cv.err$delta[1]
    [1] 0.2464536

my function:

    > fun=function(){
    +     count=0
    +     for(i in 1:length(Direction)){
    +         fit=glm(Direction~Lag1+Lag2,family = binomial,data = Weekly[-i,])
    +         prob=predict(fit,newdata = Weekly[i,],type = "response")
    +         pred="Down"
    +         if(prob>0.5)
    +             pred="Up"
    +         if(pred!=Direction[i])
    +             count=count+1
    +     }
    +     return(count/length(Direction))
    + }
    > fun()
    [1] 0.4499541

why the result is different? Could anyone explain this for me?