predict.cv.boss {BOSSreg}  R Documentation 
This function returns the prediction(s) given new observation(s) for BOSS or FS, where the optimal coefficient vector is chosen via crossvalidation.
## S3 method for class 'cv.boss' predict(object, newx, ...)
object 
The cv.boss object, returned from calling 
newx 
A new data entry or several entries. It can be a vector, or a matrix with

... 
Extra arguments to be plugged into 
The prediction for BOSS or FS.
## Generate a trivial dataset, X has mean 0 and norm 1, y has mean 0 set.seed(11) n = 20 p = 5 x = matrix(rnorm(n*p), nrow=n, ncol=p) x = scale(x, center = colMeans(x)) x = scale(x, scale = sqrt(colSums(x^2))) beta = c(1, 1, 0, 0, 0) y = x%*%beta + scale(rnorm(20, sd=0.01), center = TRUE, scale = FALSE) ## Perform 10fold CV without replication boss_cv_result = cv.boss(x, y) ## Get the coefficient vector of BOSS that gives minimum CV OSS score (S3 method for cv.boss) beta_boss_cv = coef(boss_cv_result) # the above is equivalent to boss_result = boss_cv_result$boss beta_boss_cv = boss_result$beta_boss[, boss_cv_result$i.min.boss, drop=FALSE] ## Get the fitted values of BOSSCV (S3 method for cv.boss) mu_boss_cv = predict(boss_cv_result, newx=x) # the above is equivalent to mu_boss_cv = cbind(1,x) %*% beta_boss_cv ## Get the coefficient vector of FS that gives minimum CV OSS score (S3 method for cv.boss) beta_fs_cv = coef(boss_cv_result, method='fs') ## Get the fitted values of FSCV (S3 method for cv.boss) mu_fs_cv = predict(boss_cv_result, newx=x, method='fs')