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')