plotPost {bayesboot}

Graphic display of a posterior probability distribution

Description

Plot the posterior probability distribution for a single parameter from a vector of samples, typically from an MCMC process, with appropriate summary statistics.

Usage

plotPost(paramSampleVec, credMass = 0.95, compVal = NULL, ROPE = NULL,
  HDItextPlace = 0.7, showMode = FALSE, showCurve = FALSE, ...)

Arguments

paramSampleVec

A vector of samples drawn from the target distribution.

credMass

the probability mass to include in credible intervals, or NULL to suppress plotting of credible intervals.

compVal

a value for comparison with those plotted.

ROPE

a two element vector, such as c(-1, 1), specifying the limits of the Region Of Practical Equivalence.

HDItextPlace

a value in [0,1] that controls the horizontal position of the labels at the ends of the HDI bar.

showMode

logical: if TRUE, the mode is displayed instead of the mean.

showCurve

logical: if TRUE, the posterior density will be represented by a kernel density function instead of a histogram.

...

graphical parameters and the breaks parameter for the histogram.

Details

The data are plotted either as a histogram (above) or, if showCurve = TRUE, as a fitted kernel density curve (below). Either the mean or the mode of the distribution is displayed, depending on the parameter showMode. The Highest Density Interval (HDI) is shown as a horizontal bar, with labels for the ends of the interval.

If values for a ROPE are supplied, these are shown as dark red vertical dashed lines, together with the percentage of probability mass within the ROPE. If a comparison value (compVal) is supplied, this is shown as a vertical green dotted line, together with the probability mass below and above this value.

Value

Returns an object of class histogram invisibly. Used for its plotting side-effect.

Note

The origin of this function is the BEST package which is based on Kruschke(2015, 2013).

Author(s)

John Kruschke, modified by Mike Meredith

References

Kruschke, J. K. (2015) Doing Bayesian data analysis, second edition: A tutorial with R, JAGS, and Stan. Waltham, MA: Academic Press / Elsevier.

Kruschke, J. K. (2013) Bayesian estimation supersedes the t test. Journal of Experimental Psychology: General, 142(2), 573.

See Also

For details of the HDI calculation, see hdi.

Examples

# Generate some data
tst <- rnorm(1e5, 3, 1)
plotPost(tst)
plotPost(tst, col='wheat', border='magenta')
plotPost(tst, credMass=0.8, ROPE=c(-1,1), xlab="Response variable")
plotPost(tst, showMode=TRUE, showCurve=TRUE, compVal=5.5)

# For integers:
tst <- rpois(1e5, 12)
plotPost(tst)

# A severely bimodal distribution:
tst2 <- c(rnorm(1e5), rnorm(5e4, 7))
plotPost(tst2)                  # A valid 95% CrI, but not HDI
plotPost(tst2, showCurve=TRUE)  # Correct 95% HDI