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?

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?

R's document says that delta is the raw cross-validation estimate of prediction error, which i think is prediction error rate. 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?

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?