plot.bayesQR {bayesQR} | R Documentation |
Produce quantile plots or traceplots with plot.bayesQR
Description
plot.bayesQR
is an S3 method that produces quantile plots, traceplots or posterior histograms based on the estimates obtained by the bayesQR
function.
For quantile plots, note that the more quantiles are estimated with bayesQR
, the more detailed the quantile plot will be.
Usage
## S3 method for class 'bayesQR'
plot(x, var=NULL, quantile=NULL, burnin=0, credint=c(0.025,0.975), plottype=NULL,
main=NULL, xlab=NULL, ylab=NULL, xlim=NULL, ylim=NULL, ...)
Arguments
x |
an output object of the |
var |
vector containing the index or name of the variable(s) that has to be plotted (default=all variables). |
quantile |
vector containing the quantile(s) that has to be plotted (default=all quantiles). |
burnin |
the number of burnin draws that should be discared (default=0, meaning all draws are included). |
credint |
the width of the credible interval (default=c(0.025, 0.975)). |
plottype |
should be ‘quantile’, ‘trace’ or ‘hist’. |
main |
Main title of the plot (default=""). |
xlab |
Label of the x-axis; if omitted, the value chosen based on the input data. |
ylab |
Label of the y-axis; if omitted, the value chosen based on the input data. |
xlim |
Plot region of the x-axis; if omitted, the value chosen based on the input data. |
ylim |
Plot region of the y-axis; if omitted, the value chosen based on the input data. |
... |
additional arguments that are passed to the generic |
Details
A quantile plot shows how the value of the regression parameter changes over a range of quantiles together with the associated credible interval. When the normal approximation was requested, the credible regions represent the adjusted credible intervals. Note that the more quantiles are estimated, the more detailed the quantile plot will be. The minimum number of quantiles to plot is 2. A posterior histogram provides a graphical representation of the marginal posterior distribution of the regression parameters. When the normal approximation was requested, the histogram will be overlaid with the adjusted credible intervals. A traceplot gives the evolution of the MCMC chain and can be used as graphical check of convergence. Note that more formal checks of convergence exist (see, for example, the coda package).
Value
A (series of) quantile plot(s) or a (series of) traceplot(s).
Author(s)
Dries F. Benoit
Examples
# Simulate data from heteroskedastic regression
set.seed(66)
n <- 200
X <- runif(n=n,min=0,max=10)
X <- X
y <- 1 + 2*X + rnorm(n=n, mean=0, sd=.6*X)
# Analyze 5 quantiles using default prior
# NOTE: to limit execution time of the example, ndraw is set
# to a very low value. Set value to 5000 for a better
# approximation of the posterior distirubtion.
out <- bayesQR(y ~ X, quantile=c(.05,.25,.5,.75,.95), ndraw=500)
# Check traceplot of first variable of .75 quantile regression
plot(out, var=1, quantile=.75, plottype="trace")
# Check posterior histogram of first variable of .5 quantile regression
plot(out, var=1, quantile=.5, plottype="hist")
# Create default quantile plot of first variable
plot(out, var=1, plottype="quantile")
# Create quantile plot of second variable with 90% credible interval
plot(out, var="X", credint=c(.05, .95), plottype="quantile", main="This is an example")