Getting started with fastkqr

This package provides tools for fitting kernel quantile regression.

The strengths and improvements that this package offers relative to other quantile regression packages are as follows:

Compiled Fortran code significantly speeds up the kernel quantile regression estimation process.
Solve non-crossing kernel quantile regression.

For this getting-started vignette, first, we will use a real data set named as GAGurine in the package MASS, which collects the concentration of chemical GAGs in the urine of 314 children aged 0 to 17 years. We used the concentration of GAG as the response variable.

library(fastkqr)
library(MASS)
data(GAGurine)
x <- as.matrix(GAGurine$Age)
y <- GAGurine$GAG

Then the kernel quantile regression model is formulated as the sum of check loss and an ℓ₂ penalty:

$$ \min_{\alpha\in\mathbb{R}^{n},b\in\mathbb{R}}\frac{1}{n} \sum_{i=1}^{n}\rho_{\tau}(y_{i}-b-\mathbf{K}_{i}^{\top}\alpha) +\frac{\lambda}{2} \alpha^{\top}\mathbf{K}\alpha \qquad (*). $$

`kqr()`

Given an input matrix x, a quantile level tau, and a response vector y, a kernel quantile regression model is estimated for a sequence of penalty parameter values. The other main arguments the users might supply are:

lambda: a user-supplied lambda sequence.
is_exact: exact or approximated solutions.

lambda <- 10^(seq(1, -4, length.out=10))
fit <- kqr(x, y, lambda=lambda, tau=0.1, is_exact=TRUE)

`cv.kqr()`

This function performs k-fold cross-validation (cv). It takes the same arguments as kqr.

cv.fit <- cv.kqr(x, y, lambda=lambda, tau=0.1)

Methods

A number of S3 methods are provided for nckqr object.

coef() and predict() return a matrix of coefficients and predictions ŷ given a matrix x at each lambda respectively. The optional s argument may provide a specific value of λ (not necessarily part of the original sequence).

coef <- coef(fit, s = c(0.02, 0.03))
predict(fit, x, tail(x), s = fit$lambda[2:3])
#>            s1       s2
#> [1,] 4.699973 4.700010
#> [2,] 4.699990 4.700066
#> [3,] 4.700150 4.700640
#> [4,] 4.700526 4.701990
#> [5,] 4.700924 4.703423
#> [6,] 4.703635 4.713199

`nckqr()`

Given an input matrix x, a sequence of quantile levels tau, and a response vector y, a non-crossing kernel quantile regression model is estimated for two sequences of penalty parameter values. It takes the same arguments x, y,is_exact, which are specified above. The other main arguments the users might supply are:

lambda2: a user-supplied lambda1 sequence for the L2 penalty.
lambda1: a user-supplied lambda2 sequence for the smooth ReLU penalty.

l2 <- 1e-4
tau <- c(0.1, 0.3, 0.5)
l1_list <- 10^seq(-8, 2, length.out=10)
fit1 <- nckqr(x ,y, lambda1 = l1_list, lambda2 = l2,  tau = tau)

`cv.nckqr()`

This function performs k-fold cross-validation (cv) for selecting the tuning parameter ‘lambda2’ of non-crossing kernel quantile regression. It takes the same arguments as nckqr.

l2_list <- 10^(seq(1, -4, length.out=10))
cv.fit1 <- cv.nckqr(x, y, lambda1=10, lambda2=l2_list, tau=tau)

Methods

A number of S3 methods are provided for nckqr object.

coef() and predict() return an array of coefficients and predictions ŷ given a matrix X and lambda2 at each lambda1 respectively. The optional s1 argument may provide a specific value of λ₁ (not necessarily part of the original sequence).

coef <- coef(fit1, s2=1e-4, s1 = l1_list[2:3])
predict(fit1, x, tail(x), s1=l1_list[1:3], s2=l2)
#> , , 1
#> 
#>          [,1]     [,2]     [,3]
#> [1,] 2.156783 2.437642 2.265022
#> [2,] 1.872422 1.921243 1.936153
#> [3,] 1.833562 1.962324 2.183077
#> [4,] 1.839793 2.187164 2.816320
#> [5,] 1.914857 2.510885 3.526075
#> [6,] 3.429429 5.942843 9.185686
#> 
#> , , 2
#> 
#>          [,1]     [,2]     [,3]
#> [1,] 2.156781 2.437617 2.265047
#> [2,] 1.872418 1.921228 1.936166
#> [3,] 1.833559 1.962314 2.183085
#> [4,] 1.839790 2.187162 2.816320
#> [5,] 1.914855 2.510889 3.526070
#> [6,] 3.429431 5.942845 9.185685
#> 
#> , , 3
#> 
#>          [,1]     [,2]     [,3]
#> [1,] 2.156745 2.436776 2.265679
#> [2,] 1.872316 1.920578 1.936479
#> [3,] 1.833467 1.961874 2.183233
#> [4,] 1.839730 2.187076 2.816230
#> [5,] 1.914830 2.511075 3.525834
#> [6,] 3.429600 5.943594 9.185657