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

In be-green/fqr: Fast Quantile Regression

Description

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

Usage

fit_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,
  lambda = 0,
  min_delta = 1e-10
)

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
lambda	optional lasso penalty weight
min_delta	smallest allowed step size for gradient descent

be-green/fqr documentation built on Dec. 19, 2021, 7:41 a.m.