Jenny,
"It didn't work" and "They worked" aren't very specific. Also, the package
name is ipred and the function is errorest.
The estimator entry on the man page for errorest has:
'cv' cross-validation, 'boot' bootstrap or '632plus' bias corrected
bootstrap (classification only).
Note the *or*. I tried the analysis of the iris data from the man page with
your estimator specification:
> testing <- errorest(Species ~ ., data=iris, model=lda,
+ estimator = c("boot","632plus"), predict= mypredict.lda)
> testing
Call:
errorest.data.frame(formula = Species ~ ., data = iris, model = lda,
predict = mypredict.lda, estimator = c("boot", "632plus"))
Bootstrap estimator of misclassification error
with 25 bootstrap replications
Misclassification error: 0.0235
Standard deviation: 0.0028
Call:
errorest.data.frame(formula = Species ~ ., data = iris, model = lda,
predict = mypredict.lda, estimator = c("boot", "632plus"))
.632+ Bootstrap estimator of misclassification error
with 25 bootstrap replications
Misclassification error: 0.0222
>
> unclass(testing)
$boot
$boot$error
[1] 0.02351852
$boot$sd
[1] 0.002847447
$boot$bc632plus
[1] FALSE
$boot$nboot
[1] 25
$"632plus"
$"632plus"$error
[1] 0.02222817
$"632plus"$nboot
[1] 25
$"632plus"$bc632plus
[1] TRUE
$call
errorest.data.frame(formula = Species ~ ., data = iris, model = lda,
predict = mypredict.lda, estimator = c("boot", "632plus"))
Is this consistent with your results?
Max
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