| bootpred(bootstrap) | R Documentation |
bootpred(x,y,nboot,theta.fit,theta.predict,err.meas,...)
x |
a matrix containing the predictor (regressor) values. Each row corresponds to an observation. |
y |
a vector containing the response values |
nboot |
the number of bootstrap replications |
theta.fit |
function to be cross-validated. Takes x and
y as an argument. See example below. |
theta.predict |
function producing predicted values for
theta.fit. Arguments are a matrix x of predictors and
fit object produced by theta.fit. See example below. |
err.meas |
function specifying error measure for a single
response y and prediction yhat. See examples below |
... |
any additional arguments to be passed to
theta.fit |
app.err |
the apparent error rate - that is, the mean value of
err.meas when theta.fit is applied to x and
y, and then used to predict y. |
optim |
the bootstrap estimate of optimism in app.err. A useful
estimate of prediction error is app.err+optim |
err.632 |
the ".632" bootstrap estimate of prediction error. |
Efron, B. (1983). Estimating the error rate of a prediction rule: improvements on cross-validation. J. Amer. Stat. Assoc, vol 78. pages 316-31.
Efron, B. and Tibshirani, R. (1993) An Introduction to the Bootstrap. Chapman and Hall, New York, London.
# bootstrap prediction error estimation in least squares
# regression
x <- rnorm(85)
y <- 2*x +.5*rnorm(85)
theta.fit <- function(x,y){lsfit(x,y)}
theta.predict <- function(fit,x){
cbind(1,x)%*%fit$coef
}
sq.err_function(y,yhat) { (y-yhat)^2}
results <- bootpred(x,y,20,theta.fit,theta.predict,
err.meas=sq.err)
# for a classification problem, a standard choice
# for err.meas would simply count up the
# classification errors:
miss.clas <- function(y,yhat){ 1*(yhat!=y)}
# with this specification, bootpred estimates
# misclassification rate