# boot.err: Calculate .632 and .632+ Bootstrap Error Rate In mt: Metabolomics Data Analysis Toolbox

## Description

Calculate .632 bootstrap and .632 plus bootstrap error rate.

## Usage

 `1` ```boot.err(err, resub) ```

## Arguments

 `err` Average error rate of bootstrap samples. `resub` A list including apparent error rate, class label and the predicted class label of the original training data (not resampled training data). Can be generated by `classifier`.

## Value

A list with the following components:

 `ae` Apparent error rate. `boot` Average error rate of bootstrap samples(Same as `err`) `b632 ` .632 bootstrap error rate. `b632p` .632 plus bootstrap error rate.

Wanchang Lin

## References

Witten, I. H. and Frank, E. (2005) Data Mining - Practical Machine Learning and Techniques. Elsevier.

Efron, B. and Tibshirani, R. (1993) An Introduction to the Bootstrap. Chapman & Hall.

Efron, B. and Tibshirani, R. (1997) Improvements on cross-validation: the .632+ bootstrap method. Journal of the American Statistical Association, 92, 548-560.

`classifier`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23``` ```## iris data set data(iris) x <- subset(iris, select = -Species) y <- iris\$Species ## 10 bootstrap training samples pars <- valipars(sampling = "boot", niter = 1, nreps = 10) tr.idx <- trainind(y, pars=pars)[[1]] ## bootstrap error rate err <- sapply(tr.idx, function(i){ pred <- classifier(x[i,,drop = FALSE],y[i],x[-i,,drop = FALSE],y[-i], method = "knn")\$err }) ## average bootstrap error rate err <- mean(err) ## apparent error rate resub <- classifier(x,y,method = "knn") ## err.boot <- boot.err(err, resub) ```