crossval {bootstrap} R Documentation

## K-fold Cross-Validation

### Description

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

### Usage

   crossval(x, y, theta.fit, theta.predict, ..., ngroup=n)


### Arguments

 x a matrix containing the predictor (regressor) values. Each row corresponds to an observation. y a vector containing the response values 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. ... any additional arguments to be passed to theta.fit ngroup optional argument specifying the number of groups formed . Default is ngroup=sample size, corresponding to leave-one out cross-validation.

### Value

list with the following components

 cv.fit The cross-validated fit for each observation. The numbers 1 to n (the sample size) are partitioned into ngroup mutually disjoint groups of size "leave.out". leave.out, the number of observations in each group, is the integer part of n/ngroup. The groups are chosen at random if ngroup < n. (If n/leave.out is not an integer, the last group will contain > leave.out observations). Then theta.fit is applied with the kth group of observations deleted, for k=1, 2, ngroup. Finally, the fitted value is computed for the kth group using theta.predict. ngroup The number of groups leave.out The number of observations in each group groups A list of length ngroup containing the indices of the observations in each group. Only returned if leave.out > 1. call The deparsed call

### References

Stone, M. (1974). Cross-validation choice and assessment of statistical predictions. Journal of the Royal Statistical Society, B-36, 111–147.

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

### Examples

# cross-validation of least squares regression
# note that crossval is not very efficient, and being a
#  general purpose function, it does not use the
# Sherman-Morrison identity for this special case
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
}
results <- crossval(x,y,theta.fit,theta.predict,ngroup=6)



[Package bootstrap version 2019.6 Index]