# rq.group.lin.prog: Quantile Regresion with Group Penalty using linear... In rqPen: Penalized Quantile Regression

## Description

Linear programming implementation of quantile regression with a group penalty.

## Usage

 ```1 2 3``` ```rq.group.lin.prog(x,y,groups,tau,lambda,intercept=TRUE,eps=1e-05, penalty="SCAD", a=3.7, coef.cutoff=1e-08, initial_beta=NULL, iterations=10,converge_criteria=.0001,penGroups=NULL,...) ```

## Arguments

 `x` Matrix of predictors. `y` Vector of response values. `groups` Vector assigning columns of x to groups. `tau` Conditional quantile being modelled. `lambda` Vector of lambdas. Default is for lambdas to be automatically generated. `intercept` Whether model should include an intercept. Constant does not need to be included in "x". `eps` Multiplier for smallest lambda. `penalty` Type of penalty: "LASSO", "SCAD" or "MCP". `a` Additional parameter for non-convex penalties. `coef.cutoff` Estimates below this value are set to zero. `initial_beta` Initial beta estimate. `iterations` Maximum number of iterations. `converge_criteria` Convergence criteria `penGroups` Specify which groups will be penalized. Default is to penalize all groups. `...` Additional arguments to be sent to rq.lasso.fit.

## Value

Returns the following:

 `coefficients` Coefficients of the model. `residuals` Residuals from the fitted model. `rho` Unpenalized portion of the objective function. `tau` Quantile being modelled. `n` Sample size. `intercept` Whether intercept was included in model. `penalty` Penalty used for fitting the model. `class` rqPen and rqNC

Ben Sherwood

## Examples

 ```1 2 3 4 5 6 7``` ``` ## Not run: x <- matrix(rnorm(800),nrow=100) y <- 1 + x[,1] - 3*x[,5] + rnorm(100) cv_model <- rq.group.lin.prog(x,y,groups=c(rep(1,4),rep(2,4)), tau=.5, lambda=1) ## End(Not run) ```

rqPen documentation built on May 30, 2017, 2:02 a.m.