K-fold Cross-Validation
See Efron and Tibshirani (1993) for details on this function.
crossval(x, y, theta.fit, theta.predict, ..., ngroup=n)
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 |
theta.predict |
function producing predicted values for
|
... |
any additional arguments to be passed to theta.fit |
ngroup |
optional argument specifying the number of groups formed .
Default is |
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 |
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 |
call |
The deparsed call |
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.
# 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.