Probability density function estimate from MCMC output
Displays a plot of the density estimate for each variable in x,
calculated by the density function. For discrete-valued
variables, a histogram is produced.
densplot(x, show.obs = TRUE, bwf,
ylim, xlab, ylab = "", type="l", main, right=TRUE, ...)x |
An |
show.obs |
Show observations along the x-axis |
bwf |
Function for calculating the bandwidth. If omitted, the bandwidth is calculate by 1.06 times the minimum of the standard deviation and the interquartile range divided by 1.34 times the sample size to the negative one fifth power |
ylim |
Limits on y axis. See |
xlab |
X-axis label. By default this will show the sample size
and the bandwidth used for smoothing. See |
ylab |
Y-axis label. By default, this is blank. See |
type |
Plot type. See |
main |
An overall title for the plot. See |
right |
Logical flag for discrete-valued distributions passed to
the |
.
... |
Further graphical parameters |
For discrete-valued distributions, a histogram is produced and values
are aggregated using the pretty() function. By default, tick marks
appear to the right of the corresponding bar in the histogram and give
the inclusive upper limit of the hist (right=TRUE). This can be
modified by specifying right=FALSE. In this case tick marks
appear on the left and specify the inclusive lower limit of the bar.
For continous distributions, if a variable is bounded below at 0, or bounded in the interval [0,1], then the data are reflected at the boundary before being passed to the density() function. This allows correct estimation of a non-zero density at the boundary.
You can call this function directly, but it is more usually called by
the plot.mcmc function.
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