coef.boss {BOSSreg}

Select coefficient vector(s) for BOSS.

Description

This function returns the optimal coefficient vector of BOSS selected by AICc (by default) or other types of information criterion.

Usage

## S3 method for class 'boss'
coef(
  object,
  ic = c("aicc", "bicc", "aic", "bic", "gcv", "cp"),
  select.boss = NULL,
  ...
)

Arguments

object

The boss object, returned from calling the boss function.

ic

Which information criterion is used to select the optimal coefficient vector for BOSS. The default is AICc-hdf.

select.boss

The index (or indicies) of columns in the coefficient matrix for which one wants to select. By default (NULL) it's selected by the information criterion specified in 'ic'.

...

Extra arguments (unused for now)

Details

If select.boss is specified, the function returns corresponding column(s) in the coefficient matrix.

If select.boss is unspecified, the function returns the optimal coefficient vector selected by AICc-hdf (other choice of IC can be specified in the argument ic).

Value

The chosen coefficient vector(s) for BOSS.

Examples

## 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(n, sd=0.01), center = TRUE, scale = FALSE)

## Fit the model
boss_result = boss(x, y)

## Get the coefficient vector selected by AICc-hdf (S3 method for boss)
beta_boss_aicc = coef(boss_result)
# the above is equivalent to the following
beta_boss_aicc = boss_result$beta_boss[, which.min(boss_result$IC_boss$aicc), drop=FALSE]
## Get the fitted values of BOSS-AICc-hdf (S3 method for boss)
mu_boss_aicc = predict(boss_result, newx=x)
# the above is equivalent to the following
mu_boss_aicc = cbind(1,x) %*% beta_boss_aicc

## Repeat the above process, but using Cp-hdf instead of AICc-hdf
## coefficient vector
beta_boss_cp = coef(boss_result, method.boss='cp')
beta_boss_cp = boss_result$beta_boss[, which.min(boss_result$IC_boss$cp), drop=FALSE]
## fitted values of BOSS-Cp-hdf
mu_boss_cp = predict(boss_result, newx=x, method.boss='cp')
mu_boss_cp = cbind(1,x) %*% beta_boss_cp