plotEstimator: plot SPQR estimators

In SPQR: Semi-Parametric Quantile Regression

plot SPQR estimators

Description

Computes and plots the estimated PDF/CDF/QF curves.

Usage

plotEstimator(object, X, ...)

Arguments

object	An object of class "SPQR"
X	A row vector indicating covariate values for which the conditional PDF/CDF/QF is computed and plotted.
...	Arguments passed on to predict.SPQR nY An integer number indicating length of grid when Y is not specified. Default: 101. type The function to be predicted; "PDF": probability density function, "CDF": cumulative distribution function, and "QF": the quantile function (default). tau The grid of quantiles for which the quantile function is computed. Default: seq(0.1,0.9,0.1). ci.level The credible level for computing the pointwise credible intervals. The default is 0 indicating no credible intervals should be computed. getAll If TRUE, extracts all posterior samples of the prediction. Default: FALSE.

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)


## plot estimated PDF
plotEstimator(fit, type = "PDF", X = 0)

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