rq.group.fit: Estimates a quantile regression model with a group penalized...

In rqPen: Penalized Quantile Regression

Estimates a quantile regression model with a group penalized objective function.

Description

Warning: function is no longer exported. Recommend using rq.group.pen() instead. Similar to cv.rq.pen function, but uses group penalty. Group penalties use the L1 norm instead of L2 for computational convenience. As a result of this the group lasso penalty is the same as the typical lasso penalty and thus you should only use a SCAD or MCP penalty. Only the SCAD and MCP penalties incorporate the group structure into the penalty. The group lasso penalty is implemented because it is needed for the SCAD and MCP algorithm. We use a group penalty extension of the QICD algorithm presented by Peng and Wang (2015).

Usage

rq.group.fit(
  x,
  y,
  groups,
  tau = 0.5,
  lambda,
  intercept = TRUE,
  penalty = "SCAD",
  alg = "QICD",
  a = 3.7,
  penGroups = NULL,
  ...
)

Arguments

x	Matrix of predictors.
y	Vector of responses.
groups	Vector of group assignments.
tau	Single quantile to be modeled.
lambda	Single value or seperate value for each group.
intercept	Whether intercept should be included in the model or not.
penalty	Type of penalty used: SCAD, MCP or LASSO.
alg	Type of algorithm used: QICD or LP.
a	Additional tuning parameter for SCAD and MCP.
penGroups	Vector of TRUE and FALSE entries for each group determing if they should be penalized. Default is TRUE for all groups.
...	Additional arguments sent to rq.group.lin.prog()

Value

Returns the following:

• coefficientsCoefficients of the model.

• residuals Residuals from the fitted model.

• rhoUnpenalized portion of the objective function.

• tau Quantile being modeled.

• nSample size.

• interceptWhether intercept was included in model.

Author(s)

Ben Sherwood, [e-mail] and Adam Maidman

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.

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