Usage
cv.scout(x, y, K= 10, lam1s=seq(0.001,.2,len=10),lam2s=seq(0.001,.2,len=10),p1=2,p2=1, trace = TRUE, plot=TRUE,plotSE=FALSE,rescale=TRUE,...)
Arguments
x
A matrix of predictors, where the rows are the samples and
the columns are the predictors
y
A matrix of observations, where length(y) should equal
nrow(x)
K
Number of cross-validation folds to be performed; default is
10
lam1s
The (vector of) tuning parameters for regularization of the
covariance matrix. Can be NULL if p1=NULL, since then no covariance
regularization is taking place. If p1=1 and nrow(x)500 then we really do not
recommend using p1=1, as graphical lasso can be uncomfortably slow.
lam2s
The (vector of) tuning parameters for the $L_1$ regularization of
the regression coefficients, using the regularized covariacne
matrix. Can be NULL if p2=NULL. (If p2=NULL, then non-zero lam2s
have no effect). A value of 0 will result in no
regularization.
p1
The $L_p$ penalty for the covariance regularization. Must be
one of 1, 2, or NULL. NULL corresponds to no covariance
regularization.
p2
The $L_p$ penalty for the estimation of the regression
coefficients based on the regularized covariance matrix. Must be one
of 1 (for $L_1$ regularization) or NULL (for no regularization).
trace
Print out progress as we go? Default is TRUE.
plot
If TRUE (by default), makes beautiful CV plots.
plotSE
Should those beautiful CV plots also display std error
bars for the CV? Default is FALSE
rescale
Scout rescales coefficients, by default, in order to
avoid over-shrinkage