Predict conditional mean and quantiles from a D-vine regression model

Predict conditional mean and quantiles from a D-vine regression model

# S3 method for vinereg
predict(object, newdata, alpha = 0.5, cores = 1, ...)

# S3 method for vinereg
fitted(object, alpha = 0.5, ...)

Arguments

object

an object of class vinereg.

newdata

matrix of covariate values for which to predict the quantile.

alpha

vector of quantile levels; NA predicts the mean based on an average of the 1:10 / 11-quantiles.

cores

integer; the number of cores to use for computations.

...

unused.

Value

A data.frame of quantiles where each column corresponds to one value of alpha.

See also

vinereg

Examples

# simulate data
x <- matrix(rnorm(200), 100, 2)
y <- x %*% c(1, -2)
dat <- data.frame(y = y, x = x, z = as.factor(rbinom(100, 2, 0.5)))

# fit vine regression model
(fit <- vinereg(y ~ ., dat))
#> D-vine regression model: y | x.2, x.1 
#> nobs = 100, edf = 2, cll = 12.48, caic = -20.95, cbic = -15.74 

# inspect model
summary(fit)
#>   var edf        cll      caic      cbic      p_value
#> 1   y   0 -214.61264  429.2253  429.2253           NA
#> 2 x.2   1   68.82503 -135.6501 -133.0449 8.692058e-32
#> 3 x.1   1  158.26435 -314.5287 -311.9235 8.261084e-71
plot_effects(fit)
#> `geom_smooth()` using method = 'loess' and formula = 'y ~ x'


# model predictions
mu_hat <- predict(fit, newdata = dat, alpha = NA) # mean
med_hat <- predict(fit, newdata = dat, alpha = 0.5) # median

# observed vs predicted
plot(cbind(y, mu_hat))


## fixed variable order (no selection)
(fit <- vinereg(y ~ ., dat, order = c("x.2", "x.1", "z.1")))
#> D-vine regression model: y | x.2, x.1, z.1 
#> nobs = 100, edf = 2, cll = 12.48, caic = -20.95, cbic = -15.74