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