hqreg-package: Regularization Paths for Lasso or Elastic-net Penalized Huber...

In hqreg: Regularization Paths for Lasso or Elastic-Net Penalized Huber Loss Regression and Quantile Regression

Description

Efficient algorithms for fitting regularization paths for lasso or elastic-net penalized regression models with Huber loss, quantile loss or squared loss.

Details

Package:	hqreg
Type:	Package
Version:	1.4
Date:	2017-2-15
License:	GPL-3

Very simple to use. Accepts X,y data for regression models, and produces the regularization path over a grid of values for the tuning parameter lambda. Also provides functions for plotting, prediction and parallelized cross-validation.

Author(s)

Congrui Yi <[e-mail]>

References

Yi, C. and Huang, J. (2016) Semismooth Newton Coordinate Descent Algorithm for Elastic-Net Penalized Huber Loss Regression and Quantile Regression, https://arxiv.org/abs/1509.02957
Journal of Computational and Graphical Statistics, accepted in Nov 2016
http://www.tandfonline.com/doi/full/10.1080/10618600.2016.1256816

Examples

X = matrix(rnorm(1000*100), 1000, 100)
beta = rnorm(10)
eps = 4*rnorm(1000)
y = drop(X[,1:10] %*% beta + eps) 

# Huber loss
fit1 = hqreg(X, y)
coef(fit1, 0.01)
predict(fit1, X[1:5,], lambda = c(0.02, 0.01))
cv.fit1 = cv.hqreg(X, y)
plot(cv.fit1)

# Quantile loss
fit2 = hqreg(X, y, method = "quantile", tau = 0.2)
plot(fit2)

# Squared loss
fit3 = hqreg(X, y, method = "ls", preprocess = "rescale")
plot(fit3, xvar = "norm")

Example output

 (Intercept)           V1           V2           V3           V4           V5 
-0.119291974  1.732619252 -0.426681478  0.576176197  0.194128453  0.955290890 
          V6           V7           V8           V9          V10          V11 
-1.158453748  1.413508685  1.854710598 -0.865021573 -0.269837964 -0.186852701 
         V12          V13          V14          V15          V16          V17 
 0.122506349 -0.028383190 -0.004260585  0.000000000  0.000000000  0.017169705 
         V18          V19          V20          V21          V22          V23 
 0.084450252  0.000000000  0.000000000  0.000000000  0.001322193 -0.106443158 
         V24          V25          V26          V27          V28          V29 
 0.040486728  0.068662793  0.000000000  0.000000000  0.012420121 -0.071540020 
         V30          V31          V32          V33          V34          V35 
-0.137695947  0.000000000  0.000000000 -0.013706938  0.000000000  0.107262347 
         V36          V37          V38          V39          V40          V41 
-0.138812799  0.000000000  0.000000000 -0.045209035  0.000000000  0.000000000 
         V42          V43          V44          V45          V46          V47 
-0.038549869  0.000000000  0.000000000  0.000000000  0.041904753 -0.192194691 
         V48          V49          V50          V51          V52          V53 
-0.018396979 -0.022116715  0.000000000 -0.008012430 -0.081264451  0.000000000 
         V54          V55          V56          V57          V58          V59 
-0.011461843  0.000000000  0.000000000  0.000000000  0.000000000 -0.026281253 
         V60          V61          V62          V63          V64          V65 
 0.000000000  0.000000000  0.041892953 -0.055547740 -0.070548813 -0.187539662 
         V66          V67          V68          V69          V70          V71 
 0.101963644  0.009479312  0.061493806  0.067124214 -0.120963648  0.000000000 
         V72          V73          V74          V75          V76          V77 
 0.000000000  0.039929709  0.000000000  0.120888810  0.020549725  0.000000000 
         V78          V79          V80          V81          V82          V83 
 0.000000000 -0.212999945 -0.201523270  0.000000000  0.000000000  0.000000000 
         V84          V85          V86          V87          V88          V89 
 0.033444398  0.086025653  0.000000000 -0.034458230  0.000000000  0.000000000 
         V90          V91          V92          V93          V94          V95 
-0.107665182 -0.136753950  0.089609537  0.000000000  0.000000000 -0.067132811 
         V96          V97          V98          V99         V100 
-0.073647906  0.049832625  0.000000000  0.000000000 -0.004373662 
          0.02    0.0158
[1,] -3.717533 -3.661919
[2,]  4.111381  4.062155
[3,] -4.534104 -4.714880
[4,] -4.629123 -4.747478
[5,]  2.201511  2.293704
CV fold #1
CV fold #2
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CV fold #10

hqreg documentation built on May 1, 2019, 10:21 p.m.