flare (version 1.8)

predict.slim: Prediction for an object with S3 class "slim"

Description

Predict responses for new design data.

Usage

# S3 method for slim
predict(object, newdata, lambda.idx = c(1:3), Y.pred.idx = c(1:5), ...)

Value

Y.pred

A matrix of predicted response values based on the selected models.

Arguments

object

An object with S3 class "slim"

newdata

A matrix or data frame containing predictors used for prediction.

lambda.idx

The indices of regularization parameters in the solution path to display. The default values are c(1:3).

Y.pred.idx

The indices of the predicted response vectors in the solution path to be displayed. The default values are c(1:5).

...

Arguments to be passed to methods.

Author

Xingguo Li, Tuo Zhao, Lie Wang, Xiaoming Yuan and Han Liu
Maintainer: Tuo Zhao <[e-mail]>

Details

predict.slim produces predicted values of the responses of the newdata from the estimated beta values in the object, i.e. $$ \hat{Y} = \hat{\beta}_0 + X_{new} \hat{\beta}. $$

See Also

slim and flare-package.

Examples

## load library
library(flare)
## generate data
set.seed(123)
n = 100
d = 200
d1 = 10
rho0 = 0.3
lambda = c(3:1)*sqrt(log(d)/n)
Sigma = matrix(0,nrow=d,ncol=d)
Sigma[1:d1,1:d1] = rho0
diag(Sigma) = 1
mu = rep(0,d)
X = mvrnorm(n=2*n,mu=mu,Sigma=Sigma)
X.fit = X[1:n,]
X.pred = X[(n+1):(2*n),]
eps = rt(n=n,df=n-1)
beta = c(rep(sqrt(1/3),3),rep(0,d-3))
Y.fit = X.fit%*%beta+eps

## Regression with "dantzig".
out=slim(X=X.fit,Y=Y.fit,lambda=lambda,method = "lq",q=1)

## Display results
Y=predict(out,X.pred)