bootpred function

Bootstrap Estimates of Prediction Error

Bootstrap Estimates of Prediction Error

See Efron and Tibshirani (1993) for details on this function.

bootpred(x,y,nboot,theta.fit,theta.predict,err.meas,...)

Arguments

  • 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

Returns

list with the following components - 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.

  • call: The deparsed call

References

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.

Examples

# 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
  • Maintainer: Scott Kostyshak
  • License: BSD_3_clause + file LICENSE
  • Last published: 2019-06-17