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 | disp, qsec, hp, drat 
#> nobs = 32, edf = 25.6, cll = -51.94, caic = 155.08, cbic = 192.61

summary(fit)
#>    var       edf         cll       caic       cbic      p_value
#> 1  mpg  0.000000 -100.189867 200.379733 200.379733           NA
#> 2 disp 13.187762   29.521786 -32.668047 -13.338271 9.065782e-08
#> 3 qsec  2.272103    4.454079  -4.363952  -1.033648 1.559593e-02
#> 4   hp  7.178554   10.836467  -7.315826   3.206038 3.267907e-03
#> 5 drat  2.965553    3.441702  -0.952298   3.394419 7.382604e-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 22.58394 22.45433
#> 2 22.53425 22.41825
#> 3 25.10289 24.93384
#> 4 20.70358 20.80241

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.