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An R package for D-vine copula based mean and quantile regression.
the stable release from CRAN:
install.packages("vinereg")
the latest development version:
# install.packages("remotes")
remotes::install_github("tnagler/vinereg", build_vignettes = TRUE)
See the package website.
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
For more examples, have a look at the vignettes with
vignette("abalone-example", package = "vinereg")
vignette("bike-rental", package = "vinereg")
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.
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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