rq.fit.two_pass: Quantile regression approximated w/ huber loss followed by...

In be-green/quantspace: Quantile Regression via Quantile Spacing

Quantile regression approximated w/ huber loss followed by post-processing

Description

Quantile regression approximated w/ huber loss followed by post-processing

Usage

rq.fit.two_pass(
  X,
  y,
  tau = 0.5,
  weights = NULL,
  control,
  lambda,
  intercept = NULL,
  init_beta = NULL,
  n_samples = min(c(ceiling(nrow(X)/10), 10000)),
  ...
)

Arguments

X	design matrix
y	outcome vector
tau	target quantile
weights	optional weight vector
control	ignored for now
lambda	ignored for now
intercept	optional integer indicating intercept column that identifies initial values
init_beta	initial guess at betas
n_samples	number of observations to use in "warmup" regression
...	other arguments, ignored for now

be-green/quantspace documentation built on March 20, 2024, 5:30 p.m.