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

In rqPen: Penalized Quantile Regression

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,
  ...
)

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. "QICD" or "LP".
penGroups	Specify which groups will be penalized. Default is to penalize all groups.
...	Additional arguments to be sent to rq.group.fit or groupQICDMultLambda.

Value

Returns the following:

• beta Matrix of coefficients for different values of lambda

• residuals Matrix of residuals for different values of lambda.

• rhoVector 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.

• interceptWhether intercept was included in model.

• groupsGroup structure for penalty function.

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

## Not run: 
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")

## End(Not run)

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