Plot marginal effects of a D-vine regression model

The points show fitted conditional quantiles against each requested variable. For variable \(X_k\), the smooth curve estimates \(E[\hat Q_\alpha(Y \mid X) \mid X_k = x]\). It therefore averages over the conditional distribution of the other variables. A curve for an unselected variable can vary when that variable is associated with selected predictors. The curve is descriptive and is not a partial-dependence or causal effect.

Usage

plot_effects(object, alpha = c(0.1, 0.5, 0.9), vars = object$order)

Arguments

object

a vinereg object

alpha

vector of quantile levels.

vars

vector of expanded variable names to display.

Value

A ggplot2::ggplot() object.

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)))

# fit vine regression model
fit <- vinereg(y ~ ., dat)
plot_effects(fit)
#> `geom_smooth()` using method = 'loess' and formula = 'y ~ x'