predict.vinereg: Predict conditional mean and quantiles from a D-vine...

Description Usage Arguments Value See Also Examples

View source: R/predict.vinereg.R

Description

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

Usage

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## 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

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

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# 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))

# inspect model
summary(fit)
plot_effects(fit)

# 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")))

Example output

D-vine regression model: y | x.2, x.1 
nobs = 100, edf = 2, cll = 82.91, caic = -161.82, cbic = -156.61 
  var edf        cll      caic      cbic      p_value
1   y   0 -218.26300  436.5260  436.5260           NA
2 x.2   1   83.39339 -164.7868 -162.1816 3.723935e-38
3 x.1   1  217.78132 -433.5626 -430.9575 1.000452e-96
`geom_smooth()` using method = 'loess' and formula 'y ~ x'
D-vine regression model: y | x.2, x.1, z.1 
nobs = 100, edf = 2, cll = 82.91, caic = -161.82, cbic = -156.61 

vinereg documentation built on Nov. 24, 2021, 1:08 a.m.