plot variable importance comparison by quantile

Computes the quantile ALE-induced variable importance (VI) measure for each of the covariate specified in var.index, and produces a ranking plot of the covariates using bar plot for each quantile of interest.

Usage

plotQVI(object, var.index = NULL, var.names = NULL, ...)

Arguments

object

An object of class SPQR.

var.index

A vector specifying the index of the covariates for which VI measures should be computed. Default is NULL indicating all covariates are considered.

var.names

The names of the covariates to appear in the bar plots. Default is NULL and the the function will use generic names generated by parse(text=paste0("X[",var.index,"]")).

...

Arguments passed on to QALE

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.

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

# \donttest{
set.seed(919)
n <- 200
X <- matrix(runif(n*2, 0, 2), nrow = n, ncol = 2)
Y <- rnorm(n, X[,1]^2, 0.3+X[,1]/2)
control <- list(iter = 200, warmup = 150, thin = 1)
fit <- SPQR(X=X, Y=Y, n.knots=12, n.hidden=5, method="MCMC",
            control=control, normalize=TRUE, verbose = FALSE)

## compute quantile VI of at tau = 0.2,0.5,0.8
plotQVI(fit, tau=c(0.2,0.5,0.8))

# }