prq: Profile Boosting for Quantile Regression

In pboost: Profile Boosting Framework for Parametric Models

Profile Boosting for Quantile Regression

Description

prq inherits the usage of the function quantreg::rq.

Usage

prq(
  formula,
  tau = 0.5,
  data,
  subset,
  weights,
  na.action,
  method = "br",
  model = TRUE,
  contrasts = NULL,
  ...,
  stopFun = EBIC,
  keep = NULL,
  maxK = NULL,
  verbose = FALSE
)

Arguments

formula	See pboost.
tau	Parameters passed to quantreg::rq.
data	See pboost.
subset	Parameters passed to quantreg::rq.
weights	Parameters passed to quantreg::rq.
na.action	Parameters passed to quantreg::rq.
method	Parameters passed to quantreg::rq.
model	Parameters passed to quantreg::rq.
contrasts	Parameters passed to quantreg::rq.
...	Parameters passed to quantreg::rq.
stopFun	Parameters passed to pboost.
keep	Parameters passed to pboost.
maxK	Parameters passed to pboost.
verbose	Parameters passed to pboost.

Value

An rq model object fitted on the selected features.

Examples

library(quantreg)
set.seed(2025)
n <- 300
p <- 200
x <- matrix(rnorm(n*p), n)

eta <- drop( x[, 1:3] %*% runif(3, 1.0, 1.5) )
y <- eta + (1.0 + x[, 3]) * rnorm(n)
DF <- data.frame(y, x)

tau <- 0.5
prq(y ~ ., tau, DF, verbose=TRUE)

BIC <- function(obj) AIC(obj, k=-1)
prq(y ~ ., tau, DF, stopFun=BIC, verbose=TRUE)

scorerq <- function(object) {
 return(ifelse(object[["y"]] < fitted(object), tau - 1, tau))
}
pboost(y ~ ., DF, rq, scorerq, EBIC, tau=tau, verbose=TRUE)

pboost documentation built on Jan. 9, 2026, 1:07 a.m.