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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.
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