crossval {bootstrap} | R Documentation |
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 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. |
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 . |
Stone, M. (1974). Cross-validation choice and assessment of statistical predictions. Journal of the Royal Statistical Society, B-36, 111147.
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)