predict.boss {BOSSreg} R Documentation

## Prediction given new data entries.

### Description

This function returns the prediction(s) given new observation(s), for BOSS, where the optimal coefficient vector is chosen via certain selection rule.

### Usage

```## S3 method for class 'boss'
predict(object, newx, ...)
```

### Arguments

 `object` The boss object, returned from calling 'boss' function. `newx` A new data entry or several entries. It can be a vector, or a matrix with `nrow(newx)` being the number of new entries and `ncol(newx)=p` being the number of predictors. The function takes care of the intercept, NO need to add `1` to `newx`. `...` Extra arguments to be plugged into `coef`, such as `select.boss`, see the description of `coef.boss` for more details.

### Details

The function basically calculates x * coef, where `coef` is a coefficient vector chosen by a selection rule. See more details about the default and available choices of the selection rule in the description of `coef.boss`.

### Value

The prediction(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
```

[Package BOSSreg version 0.2.0 Index]