sqr.fit: Spline Quantile Regression (SQR)

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

Spline Quantile Regression (SQR)

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.fit(
  X,
  y,
  tau,
  spar = 1,
  d = 1,
  weighted = FALSE,
  mthreads = TRUE,
  ztol = 1e-05
)

Arguments

X	design matrix (nrow(X) = length(y))
y	response vector
tau	sequence of quantile levels in (0,1)
spar	smoothing parameter
d	subsampling rate of quantile levels (default = 1)
weighted	if TRUE, penalty function is weighted (default = FALSE)
mthreads	if FALSE, set RhpcBLASctl::blas_set_num_threads(1) (default = TRUE)
ztol	zero tolerance parameter used to determine the effective dimensionality of the fit

Value

A list with the following elements:

coefficients	matrix of regression coefficients
crit	sequence critera for smoothing parameter select: (AIC,BIC)
np	sequence of number of effective parameters
fid	sequence of fidelity measure as quasi-likelihood
nit	number of iterations

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