predict.slim | R Documentation |
"slim"
Predicting responses of the given design data.
## S3 method for class 'slim' predict(object, newdata, lambda.idx = c(1:3), Y.pred.idx = c(1:5), ...)
object |
An object with S3 class |
newdata |
An optional data frame in which to look for variables with which to predict. If omitted, the traning data of the are used. |
lambda.idx |
The indices of the regularizaiton parameters in the solution path to be displayed. The default values are |
Y.pred.idx |
The indices of the predicted response vectors in the solution path to be displayed. The default values are |
... |
Arguments to be passed to methods. |
predict.slim
produces predicted values of the responses of the newdata
from the estimated beta
values in the object
, i.e.
\hat{Y} = \hat{β}_0 + X_{new} \hat{β}.
Y.pred |
The predicted response vectors based on the estimated models. |
Xingguo Li, Tuo Zhao, Lie Wang, Xiaoming Yuan and Han Liu
Maintainer: Xingguo Li <xingguo.leo@gmail.com>
slim
and flare-package
.
## 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)
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