Description Usage Arguments Details Value Note Author(s) References See Also Examples
Plot the posterior probability distribution for a single parameter from a vector of samples, typically from an MCMC process, with appropriate summary statistics.
1 2 |
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 |
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 |
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.
Returns an object of class histogram
invisibly. Used for its
plotting side-effect.
The origin of this function is the BEST package which is based on Kruschke(2015, 2013).
John Kruschke, modified by Mike Meredith
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.
For details of the HDI calculation, see hdi
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | # 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
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