fit_penalize_approx_quantile_model: Compute quantile regression via accelerated gradient descent...

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

Compute quantile regression via accelerated gradient descent using Huber approximation, warm start based on data subset

Description

Compute quantile regression via accelerated gradient descent using Huber approximation, warm start based on data subset

Usage

fit_penalize_approx_quantile_model(
  X,
  y,
  X_sub,
  y_sub,
  tau,
  init_beta,
  mu = 1e-15,
  maxiter = 100000L,
  beta_tol = 1e-04,
  check_tol = 1e-06,
  intercept = 1L,
  num_samples = 1000,
  warm_start = 1L,
  scale = 1L
)

Arguments

X	design matrix
y	outcome vector
X_sub	subset of X matrix to use for "warm start" regression
y_sub	subset of y to use for "warm start" regression
tau	target quantile
init_beta	initial guess at beta
mu	neighborhood over which to smooth
maxiter	maximum number of iterations to run
beta_tol	tolerance for largest element of gradient, used for early stopping
check_tol	loss function change tolerance for early stopping
intercept	location of the intercept column, using R's indexing
num_samples	number of samples used for subset of matrix used for warm start
warm_start	integer indicating whether to "warm up" on a subsample of the data
scale	whether to scale x & y variables

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