cv.scout: Perform cross-validation for covariance-regularized... In scout: Implements the Scout Method for Covariance-Regularized Regression

Description

This function returns cross-validation error rates for a range of lambda1 and lambda2 values, and also makes beautiful CV plots if plot=TRUE.

Usage

 ```1 2 3``` ```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 `...` Additional parameters

Details

Pass in a data matrix x and a vector of outcomes y; it will perform (10-fold) cross-validation over a range of lambda1 and lambda2 values. By default, Scout(2,1) is performed.

Value

 `folds` The indices of the members of the K test sets are returned. `cv` A matrix of average cross-validation errors is returned. `cv.error` A matrix containing the standard errors of the elements in "cv", the matrix of average cross-validation errors. `bestlam1` Best value of lam1 found via cross-validation. `bestlam2` Best value fo lam2 found via cross-validation. `lam1s` Values of lam1 considered. `lam2s` Values of lam2 considered.

Author(s)

Daniela M. Witten and Robert Tibshirani

References

Witten, DM and Tibshirani, R (2008) Covariance-regularized regression and classification for high-dimensional problems. Journal of the Royal Statistical Society, Series B 71(3): 615-636. <http://www-stat.stanford.edu/~dwitten>

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```library(lars) data(diabetes) attach(diabetes) par(mfrow=c(2,1)) par(mar=c(2,2,2,2)) ## Not run: cv.sc <- cv.scout(x2,y,p1=2,p2=1) ## Not run: print(cv.sc) ## Not run: cv.la <- cv.lars(x2,y) ## Not run: print(c("Lars minimum CV is ", min(cv.la\$cv))) ## Not run: print(c("Scout(2,1) minimum CV is ", min(cv.sc\$cv))) detach(diabetes) ```