rq.fit.agd: Quantile Regression approximated w/ huber loss

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

Quantile Regression approximated w/ huber loss

Description

Quantile Regression approximated w/ huber loss

Usage

rq.fit.agd(
  X,
  y,
  tau = 0.5,
  weights = NULL,
  control,
  lambda,
  smoothing_window = 1e-10,
  beta_tol = 1e-05,
  check_tol = 1e-05,
  maxiter = 1000,
  n_samples = min(c(ceiling(nrow(X)/10), 10000)),
  init_beta = NULL,
  scale = 1,
  intercept = NULL,
  ...
)

Arguments

X	design matrix
y	outcome vector
tau	target quantile
weights	optional weight vector
control	ignored for now
lambda	ignored for now
smoothing_window	neighborhood around 0 which is smoothed by either typical least squares or appropriately tilted least squares loss function
beta_tol	stopping rule based on max value of gradient
check_tol	stopping rule based on change in the loss function
maxiter	largest number of iterations allowed
n_samples	number of observations to use in "warmup" regression
init_beta	initial guess at betas
scale	whether to scale x and y variables in regression
intercept	optional integer indicating intercept column that identifies initial values
...	other arguments, ignored for now

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