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vinereg

Fit D-vine copula regression models for conditional mean and quantile prediction with continuous or discrete variables.

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

vinereg provides:

• automatic covariate selection or a user-specified D-vine order;
• parametric and nonparametric pair-copula families;
• conditional mean, quantile, distribution, and density predictions;
• support for continuous, ordered, and unordered variables; and
• standard model summaries, information criteria, and marginal effect plots.

See the package website for the complete reference and worked examples.

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_set = "nonpar", data = mtcars))
#> D-vine regression model: mpg | wt, qsec, drat
#> nobs = 32, edf = 20.35, cll = -57.42, caic = 155.54, cbic = 185.36

summary(fit)
#>    var      edf         cll       caic       cbic      p_value
#> 1  mpg 3.803013 -100.046939 207.699904 213.274116           NA
#> 2   wt 9.871177   29.583463 -39.424574 -24.956036 4.600863e-09
#> 3 qsec 5.389674    7.422915  -4.066482   3.833357 1.449560e-02
#> 4 drat 1.282135    5.617764  -8.671258  -6.791987 1.321129e-03
AIC(fit)
#> [1] 155.5376

# show marginal effects for all selected variables
plot_effects(fit)

# predict mean and median
head(predict(fit, mtcars, alpha = c(NA, 0.5)), 4)
#>       mean      0.5
#> 1 22.57023 22.28455
#> 2 21.76349 21.46394
#> 3 25.51574 25.26815
#> 4 20.18286 20.17140

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. arXiv preprint, preprint.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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