D-Vine Quantile Regression.

vinereg

An R package for D-vine copula based mean and quantile regression.

How to install

• the stable release from CRAN:

  install.packages("vinereg")
  

• the latest development version:

  # install.packages("remotes")
  remotes::install_github("tnagler/vinereg", build_vignettes = TRUE)
  

Functionality

See the package website.

Example

set.seed(5)
library(vinereg)
data(mtcars)

# declare factors and discrete variables
for (var in c("cyl", "vs", "gear", "carb"))
    mtcars[[var]] <- as.ordered(mtcars[[var]])
mtcars[["am"]] <- as.factor(mtcars[["am"]])

# fit model
(fit <- vinereg(mpg ~ ., family = "nonpar", data = mtcars))
#> D-vine regression model: mpg | wt, qsec, drat, gear 
#> nobs = 32, edf = 23.63, cll = -55.86, caic = 158.98, cbic = 193.62

summary(fit)
#>    var       edf         cll       caic        cbic      p_value
#> 1  mpg  0.000000 -100.135440 200.270879 200.2708794           NA
#> 2   wt 11.452248   28.706110 -34.507723 -17.7217520 4.161832e-08
#> 3 qsec  6.091637    7.596924  -3.010573   5.9181583 1.990142e-02
#> 4 drat  5.089693    5.742895  -1.306405   6.1537401 4.494112e-02
#> 5 gear  1.000000    2.232423  -2.464845  -0.9991094 3.459922e-02

# show marginal effects for all selected variables
plot_effects(fit)
#> `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

# predict mean and median
head(predict(fit, mtcars, alpha = c(NA, 0.5)), 4)
#>       mean      0.5
#> 1 23.38467 23.04676
#> 2 22.69125 22.36638
#> 3 26.29842 26.10553
#> 4 20.62143 20.63283

Vignettes

For more examples, have a look at the vignettes with

vignette("abalone-example", package = "vinereg")
vignette("bike-rental", package = "vinereg")

References

Kraus and Czado (2017). D-vine copula based quantile regression. Computational Statistics & Data Analysis, 110, 1-18. link, preprint

Schallhorn, N., Kraus, D., Nagler, T., Czado, C. (2017). D-vine quantile regression with discrete variables. Working paper, preprint.