rqPen (version 4.1.3)

cv.rq.group.pen: Old cross validation function for group penalty

Description

This function is no longer exported. Recommend using rq.group.pen.cv() instead.

Usage

cv.rq.group.pen(
  x,
  y,
  groups,
  tau = 0.5,
  lambda = NULL,
  penalty = "SCAD",
  intercept = TRUE,
  criteria = "CV",
  cvFunc = "check",
  nfolds = 10,
  foldid = NULL,
  nlambda = 100,
  eps = 1e-04,
  init.lambda = 1,
  alg = "huber",
  penGroups = NULL,
  ...
)

Value

Returns the following:

beta

Matrix of coefficients for different values of lambda

residuals

Matrix of residuals for different values of lambda.

rho

Vector of rho, unpenalized portion of the objective function, for different values of lambda.

cv

Data frame with "lambda" and second column is the evaluation based on the criteria selected.

lambda.min

Lambda which provides the smallest statistic for the selected criteria.

penalty

Penalty selected.

intercept

Whether intercept was included in model.

groups

Group structure for penalty function.

Arguments

x

Matrix of predictors.

y

Vector of responses.

groups

Vector of groups.

tau

Quantile being modeled.

lambda

Vector of lambdas. Default is for lambdas to be automatically generated.

penalty

Type of penalty: "LASSO", "SCAD" or "MCP".

intercept

Whether model should include an intercept. Constant does not need to be included in "x".

criteria

How models will be evaluated. Either cross-validation "CV", BIC "BIC" or large P BIC "PBIC".

cvFunc

If cross-validation is used how errors are evaluated. Check function "check", "SqErr" (Squared Error) or "AE" (Absolute Value).

nfolds

K for K-folds cross-validation.

foldid

Group id for cross-validation. Function will randomly generate groups if not specified.

nlambda

Number of lambdas for which models are fit.

eps

Multiple of lambda max for Smallest lambda used.

init.lambda

Initial lambda used to find the maximum lambda. Not needed if lambda values are set.

alg

Algorithm used for fit. Only "LP", "QICD" is no longer available.

penGroups

Specify which groups will be penalized. Default is to penalize all groups.

...

Additional arguments to be sent to rq.group.fit

References

• Yuan, M. and Lin, Y. (2006). Model selection and estimation in regression with grouped variables. J. R. Statist. Soc. B, 68, 49-67.

• Peng, B. and Wang, L. (2015). An Iterative Coordinate Descent Algorithm for High-Dimensional Nonconvex Penalized Quantile Regression. Journal of Computational and Graphical Statistics, 24, 676-694.

Examples

if (FALSE) {
x <- matrix(rnorm(800),nrow=100)
y <- 1 + x[,1] - 3*x[,5] + rnorm(100)
cv_model <- cv.rq.group.pen(x,y,groups=c(rep(1,4),rep(2,4)),criteria="BIC")
}