plotQALE: plot accumulated local effects (ALE)

In SPQR: Semi-Parametric Quantile Regression

plot accumulated local effects (ALE)

Description

Computes and plots the quantile ALEs of a SPQR class object. The function plots the ALE main effects across tau for a single covariate using line plots, and the ALE interaction effects between two covariates across tau using contour plots.

Usage

plotQALE(object, ...)

Arguments

object

An object of class "SPQR".

...

Arguments passed on to QALE var.index a numeric scalar or length-two vector of indices of the covariates for which the ALEs will be calculated. When length(var.index) = 1, the function computes the main effect for X[,var.index]. When length(var.index) = 2, the function computes the interaction effect between X[,var.index[1]] and X[,var.index[2]]. tau The quantiles of interest. n.bins the maximum number of intervals into which the covariate range is divided when calculating the ALEs. The actual number of intervals depends on the number of unique values in X[,var.index]. When length(var.index) = 2, n.bins is applied to both covariates. ci.level The credible level for computing the pointwise credible intervals for ALE when length(var.index) = 1. The default is 0 indicating no credible intervals should be computed. getAll If TRUE and length(var.index) = 1, extracts all posterior samples of ALE. pred.fun A function that will be used instead of predict.SPQR() for computing predicted quantiles given covariates. This can be useful when the user wants to compare the QALE calculated using SPQR to that using other quantile regression models, or maybe that using the true model in a simulation study.

Value

A ggplot object.

Examples

set.seed(919)
n <- 200
X <- runif(n,0,2)
Y <- rnorm(n,X^2,0.3+X/2)
control <- list(iter = 200, warmup = 150, thin = 1)
fit <- SPQR(X=X, Y=Y, n.knots=12, n.hidden=3, method="MCMC",
            control=control, normalize=TRUE)

## compute quantile ALE main effect of X at tau = 0.2,0.5,0.8
plotQALE(fit, var.index=1, tau=c(0.2,0.5,0.8))

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