vinereg

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 
#> nobs = 32, edf = 19.33, cll = -56.94, caic = 152.55, cbic = 180.88

summary(fit)
#>    var       edf         cll       caic       cbic      p_value
#> 1  mpg  0.000000 -100.189867 200.379733 200.379733           NA
#> 2   wt 10.597257   29.409422 -37.624331 -22.091551 1.062440e-08
#> 3 qsec  5.739523    7.867765  -4.256484   4.156141 1.286392e-02
#> 4 drat  2.996463    5.973303  -5.953681  -1.561657 7.542848e-03

# 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 23.33594 22.56025
#> 2 22.30219 21.68133
#> 3 25.71678 25.58609
#> 4 20.21699 20.36290

vignette("abalone-example", package = "vinereg")
vignette("bike-rental", package = "vinereg")