| estim.regul {integrOmics} | R Documentation |
Computes leave-one-out or M-fold cross-validation scores on a two-dimensional
grid to determine optimal values for the parameters of regularization in
rcc.
estim.regul(X, Y, grid1 = NULL, grid2 = NULL,
validation = c("loo", "Mfold"),
folds, M = 10, plt = TRUE, ...)
X |
numeric matrix or data frame (n times p), the observations on the X variables.
NAs are allowed. |
Y |
numeric matrix or data frame (n times q), the observations on the Y variables.
NAs are allowed. |
grid1, grid2 |
vector numeric defining the values of lambda1 and lambda2
at which cross-validation score should be computed. Defaults to
lambda1 = lambda2 = seq(from=0.001, to=1, length=5). |
validation |
character string. What kind of (internal) cross-validation method to use,
(partially) matching one of "loo" (leave-one-out) or "Mfolds" (M-folds). See Details. |
folds |
list of vectors (as returned by split)
containing the indices for the validation sample (see Details). |
M |
positive integer. Number of folds to use if validation="Mfold". Defaults to
M=10. |
plt |
logical argument indicating whether a image map should be
plotted by calling the imgCV function. |
... |
not used currently. |
If validation="Mfolds", M-fold cross-validation is performed by calling
Mfold. When folds is given, the elements of folds should be integer vectors
specifying the indices of the validation sample and the argument M is
ignored. Otherwise, the folds are generated. The number of cross-validation
folds is specified with the argument M.
If validation="loo",
leave-one-out cross-validation is performed by calling the
loo function. In this case the arguments folds and M are ignored.
The returned value is a list with components:
opt.lambda1, |
|
opt.lambda2 |
value of the parameters of regularization on which the cross-validation method reached it optimal. |
opt.score |
the optimal cross-validation score reached on the grid. |
grid1, grid2 |
original vectors grid1 and grid2. |
mat |
matrix containing the cross-validation score computed on the grid. |
Sébastien Déjean and Ignacio González.
loo, Mfold, image.estim.regul.
data(nutrimouse) X <- nutrimouse$lipid Y <- nutrimouse$gene ## this can take some seconds estim.regul(X, Y, validation = "Mfold")