QICD.nonpen: Implements QICD algorithm with some variables not being...

In rqPen: Penalized Quantile Regression

Implements QICD algorithm with some variables not being penalized

Description

Implements QICD algorithm with some variables not being penalized

Usage

QICD.nonpen(
  y,
  x,
  z,
  tau = 0.5,
  lambda,
  intercept = TRUE,
  penalty = "SCAD",
  initial_beta = NULL,
  maxin = 100,
  maxout = 20,
  eps = 1e-05,
  coef.cutoff = 1e-08,
  a = 3.7,
  method = "br",
  scalex = TRUE,
  ...
)

Arguments

y	response variable, length n vector
x	input nxp matrix, of dimension nobs x nvars; each row is an observation vector.
z	nxq matrix of bases; the coefficients for these columns will be unpenalized
tau	the quantile value
lambda	the tuning parameter (numeric value > 0)
intercept	a logical value,should intercept be fitted (default=TRUE) (intercept should be included when using splines)
penalty	The name of the penalty function ("SCAD", "MCP", "LASSO")
initial_beta	Vector containing initial values for intercept (if included) and x coefficients. Should be in the form (intercept, coefficients) intercept should be left out if intercept=FALSE. The intercept should be included to be consistent with other methods, but intercept and z coefficients will be initialized to by a rq() fit of residuals from initial beta against the unpenalized predictors, z.
maxin	maximum number of iterations for inside coordinate descent,default value is 100
maxout	maximum number of iterations for outside MM step, default value is 20
eps	The convergence threshold for coordinate descent and majorization minimization step
coef.cutoff	Threshold for determining nonzero coefficients
a	Scale parameter, the default value is 3.7 (>2 for SCAD, >1 for MCP, not used in LASSO)
method	quantile regression initialization method, can be "br" or "fn".
scalex	Whether predictors are centered and scaled
...	additional parameters

rqPen documentation built on Aug. 25, 2023, 1:07 a.m.