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 = 18.39, cll = -50.08, caic = 136.93, cbic = 163.88

summary(fit)
#>    var      edf         cll       caic       cbic      p_value
#> 1  mpg 0.000000 -100.135440 200.270879 200.270879           NA
#> 2 disp 8.391335   31.185601 -45.588532 -33.289052 2.446502e-10
#> 3 qsec 1.624310    3.907191  -4.565762  -2.184953 1.300182e-02
#> 4   hp 7.371096   11.928452  -9.114713   1.689367 1.576882e-03
#> 5 drat 1.000000    3.038036  -4.076071  -2.610335 1.370252e-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 22.46158 22.35297
#> 2 22.45410 22.35836
#> 3 24.89114 24.60640
#> 4 20.44469 20.44982

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.