predict.rq.pen.seq.cv: Predictions from rq.pen.seq.cv object

In bssherwood/rqpen: Penalized Quantile Regression

Predictions from rq.pen.seq.cv object

Description

Predictions from rq.pen.seq.cv object

Usage

## S3 method for class 'rq.pen.seq.cv'
predict(
  object,
  newx,
  tau = NULL,
  septau = ifelse(object$fit$penalty != "gq", TRUE, FALSE),
  cvmin = TRUE,
  useDefaults = TRUE,
  ...
)

Arguments

object	rq.pen.seq.cv object
newx	Matrix of predictors
tau	Quantile of interest. Default is NULL, which will return all quantiles. Should not be specified if modelsIndex is used.
septau	Whether tuning parameter should be optimized separately for each quantile.
cvmin	If TRUE then minimum error is used, if FALSE then one standard error rule is used.
useDefaults	Whether the default results are used. Set to FALSE if you you want to specify specific models and lambda values.
...	Additional parameters sent to coef.rq.pen.seq.cv().

Value

A matrix of predictions for each tau and a combination

Author(s)

Ben Sherwood, [e-mail]

Examples

x <- matrix(runif(1600),ncol=8)
y <- 1 + x[,1] + x[,8] + (1+.5*x[,3])*rnorm(200)
m1 <- rq.pen.cv(x,y,penalty="ENet",a=c(0,.5,1),tau=c(.25,.75),lambda=c(.1,.05,.01))
newx <- matrix(runif(80),ncol=8)
cvpreds <- predict(m1,newx)

bssherwood/rqpen documentation built on April 23, 2024, 9:50 a.m.