autoplot.SPQR: autoplot method for class 'SPQR'

In SPQR: Semi-Parametric Quantile Regression

autoplot method for class SPQR

Description

The function calls one of the following functions: plotEstimator(), plotGOF(), plotMCMCtrace(), plotQALE(), plotQVI()

Usage

## S3 method for class 'SPQR'
autoplot(object, output = c("GOF", "estimator", "trace", "QALE", "QVI"), ...)

Arguments

object	An object of class SPQR.
output	A character indicating the type of plot to be returned. "GOF": goodness of fit test by comparing the quantiles of probability integral transform (PIT) to that of uniform distribution. "estimator": visualization of various estimates, including probability density function (PDF), cumulative density function (CDF) and quantile function (QF). "trace": diagnostic trace plots for SPQR fitted with method = "MCMC". "QALE": quantile accumulative local effects (ALE) for visualizing covariate effects on predicted quantiles. "QVI": quantile variable importance comparison.
...	arguments passed into specific plot function, see plotEstimator(), plotGOF(), plotMCMCtrace(), plotQALE() or plotQVI() for required arguments.

Value

a ggplot object

Examples

set.seed(919)
n <- 200
X <- rbinom(n, 1, 0.5)
Y <- rnorm(n, X, 0.8)
control <- list(iter = 200, warmup = 150, thin = 1)
fit <- SPQR(X = X, Y = Y, method = "MCMC", control = control,
            normalize = TRUE, verbose = FALSE)

## Goodness-of-fit test
autoplot(fit, output = "GOF")

SPQR documentation built on May 3, 2022, 1:08 a.m.