rq.lasso.fit: Quantile Regression with LASSO penalty

Description Usage Arguments Value Author(s) References Examples

View source: R/workHorse.R

Description

Fits a quantile regression model with the LASSO penalty. Algorithm is similar to LASSO code presented in Koenker and Mizera (2014).

Usage

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rq.lasso.fit(x,y,tau=.5,lambda=NULL,weights=NULL,
             intercept=TRUE,coef.cutoff=.00000001,
             method=NULL, penVars=NULL, ...)

Arguments

x

Matrix of predictors.

y

Vector of response values.

tau

Conditional quantile being modelled.

lambda

Tuning parameter.

weights

Weights for the objective function.

intercept

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

coef.cutoff

Coefficients below this value will be set to zero.

method

Use method "br" or "fn" as outlined in quantreg package. Method "fn" better for large n or p. Default will choose "br" or "fn" based on n and p.

penVars

Variables that should be penalized. With default value of NULL all variables are penalized.

...

Additional items to be sent to rq. Note this will have to be done carefully as rq is run on the augmented data to account for penalization and could provide strange results if this is not taken into account.

Value

Returns the following:

coefficients

Coefficients from the penalized model.

PenRho

Penalized objective function value.

residuals

Residuals from the model.

rho

Objective function evaluation without the penalty.

tau

Conditional quantile being modelled.

n

Sample size.

Author(s)

Ben Sherwood

References

[1] Koenker, R. and Mizera, I. (2014). Convex optimization in R. Journal of Statistical Software, 60, 1–23.

[2] Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B, 58, 267–288.

[3] Wu, Y. and Liu, Y. (2009). Variable selection in quantile regression. Statistica Sinica, 19, 801–817.

Examples

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x <- matrix(rnorm(800),nrow=100)
y <- 1 + x[,1] - 3*x[,5] + rnorm(100)
lassoModel <- rq.lasso.fit(x,y,lambda=1)

Example output

Loading required package: quantreg
Loading required package: SparseM

Attaching package: 'SparseM'

The following object is masked from 'package:base':

    backsolve

Loading required package: regpro
Loading required package: denpro
Warning message:
In rq.fit.br(x, y, tau = tau, ...) : Solution may be nonunique

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