Cross validation for the ridge regression is performed. There is an option for the GCV criterion which is automatic.
ridge.tune(y, x, nfolds = 10, lambda = seq(0, 2, by = 0.1), folds = NULL,
ncores = 1, seed = FALSE, graph = FALSE)A numeric vector containing the values of the target variable. If the values are proportions or percentages, i.e. strictly within 0 and 1 they are mapped into R using the logit transformation.
A numeric matrix containing the variables.
The number of folds in the cross validation.
A vector with the a grid of values of
If you have the list with the folds supply it here. You can also leave it NULL and it will create folds.
The number of cores to use. If it is more than 1 parallel computing is performed.
If seed is TRUE the results will always be the same.
If graph is set to TRUE the performances for each fold as a function of the
A list including:
The performance of the ridge regression for every fold.
The values of the mean prediction error for each value of
The value of
The minimum MSPE.
The time required by the cross-validation procedure.
A k-fold cross validation is performed. This function is used by alfaridge.tune.
Hoerl A.E. and R.W. Kennard (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1):55-67.
Brown P. J. (1994). Measurement, Regression and Calibration. Oxford Science Publications.
# NOT RUN {
y <- as.vector(iris[, 1])
x <- as.matrix(iris[, 2:4])
ridge.tune( y, x, nfolds = 10, lambda = seq(0, 2, by = 0.1), graph = TRUE )
# }
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