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

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

Usage

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

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

Arguments

object

an object of class vinereg.

newdata

a data frame containing the covariates from the original formula, with matching classes and factor levels. If omitted, the model frame used for fitting is used.

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 with one row per observation and one column per value of alpha. Columns are named by their quantile level; the conditional mean column is named mean.

See also

vinereg

Examples

# simulate data
x <- matrix(rnorm(100), 50, 2)
y <- x %*% c(1, -2)
dat <- data.frame(y = y, x = x, z = as.factor(rbinom(50, 2, 0.5)))

## fixed variable order (no selection)
(fit <- vinereg(y ~ ., dat, order = c("x.2", "x.1", "z")))
#> D-vine regression model: y | x.2, x.1, z.1, z.2
#> nobs = 50, edf = 7.16, cll = 4.68, caic = 4.95, cbic = 18.63

# 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))