sqr: Spline Quantile Regression (SQR) by formula

In qfa: Quantile-Frequency Analysis (QFA) of Time Series

Spline Quantile Regression (SQR) by formula

Description

This function computes spline quantile regression (SQR) solution from response vector and design matrix. It uses the FORTRAN code rqfnb.f in the "quantreg" package with the kind permission of Dr. R. Koenker.

Usage

sqr(
  formula,
  tau = seq(0.1, 0.9, 0.2),
  spar = NULL,
  d = 1,
  data,
  subset,
  na.action,
  model = TRUE,
  weighted = FALSE,
  mthreads = TRUE,
  method = c("AIC", "BIC"),
  ztol = 1e-05
)

Arguments

formula	a formula object, with the response on the left of a ~ operator, and the terms, separated by + operators, on the right.
tau	sequence of quantile levels in (0,1)
spar	smoothing parameter: if spar=NULL, smoothing parameter is selected by method
d	subsampling rate of quantile levels (default = 1)
data	a data.frame in which to interpret the variables named in the formula
subset	an optional vector specifying a subset of observations to be used
na.action	a function to filter missing data (see rq in the 'quantreg' package)
model	if TRUE then the model frame is returned (needed for calling summary subsequently)
weighted	if TRUE, penalty function is weighted (default = FALSE)
mthreads	if FALSE, set RhpcBLASctl::blas_set_num_threads(1) (default = TRUE)
method	a criterion for smoothing parameter selection if spar=NULL ("AIC" or "BIC")
ztol	a zero tolerance parameter used to determine the effective dimensionality of the fit

Value

object of SQR fit

Examples

library(quantreg)
data(engel)
engel$income <- engel$income - mean(engel$income)
tau <- seq(0.1,0.9,0.05)
fit <- rq(foodexp ~ income,tau=tau,data=engel)
fit.sqr <- sqr(foodexp ~ income,tau=tau,spar=0.5,data=engel)
par(mfrow=c(1,1),pty="m",lab=c(10,10,2),mar=c(4,4,2,1)+0.1,las=1)
plot(tau,fit$coef[2,],xlab="Quantile Level",ylab="Coeff1")
lines(tau,fit.sqr$coef[2,])

qfa documentation built on April 11, 2025, 5:49 p.m.