densplot: Probability density function estimate from MCMC output

densplotR Documentation

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, ...)



An mcmc or mcmc.list object


Show observations along the x-axis


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


Limits on y axis. See plot.window


X-axis label. By default this will show the sample size and the bandwidth used for smoothing. See plot


Y-axis label. By default, this is blank. See plot


Plot type. See plot


An overall title for the plot. See title


Logical flag for discrete-valued distributions passed to the hist function. See Details



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.

See Also

density, hist, plot.mcmc.

coda documentation built on May 29, 2024, 11:23 a.m.

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