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Unknown | 36 lines | 34 code | 2 blank | 0 comment | 0 complexity | f5fd060f584565d7dd578314816a7188 MD5 | raw file
 3\title{plotting of Bayesian posterior samples}
 4\description{Compares posterior densities of Bayesian estimates}
 5\usage{trace.plot(obj, col, alpha, lwd = 1, hpd = 0.95, bars = TRUE, legend.control = list(plot = TRUE, pos = NA, cex = 1, pt.cex = 1, pch = 22, title = ""), truncate = list(min = NA, max = NA), xlim = list(min = NA, max = NA), ...)}
 7  \item{obj}{a vector or dataframe (where each column is separately plotted); 
 8  names of columns are retrieved if a legend is to be generated}
 9  \item{col}{either empty or a vector of colors with one for each column in the \code{obj}}
10  \item{alpha}{a degree of color translucence to be used for shaded densities, where 1 is opaque, 0 is clear}
11  \item{lwd}{line width used for highest-density range and density outlines (see \code{\link{par}})}
12  \item{hpd}{either \code{NULL} or a value between 0 and 1, corresponding to the width of the highest 
13  density region to be shaded}
14  \item{bars}{a logical specifier which, if \code{bars=TRUE}, outlines the width of the high density region}
15  \item{legend.control}{if \code{plot=TRUE}, a legend is generated (see \code{\link{legend}} for details concerning this list)}
16  \item{truncate}{if \code{min} and (or) \code{max} are defined, values below and (or) above the truncation 
17  point are removed prior to computation of the highest density region and prior to plotting}
18  \item{xlim}{if \code{min} and (or) \code{max} are defined, values below and (or) above the limit(s) are 
19  excluded from the plot without further corrupting the data}
20  \item{\dots}{further arguments to be passed to \code{\link{plot}}}
22\author{JM Eastman}
24\seealso{\code{\link[diversitree]{profiles.plot}}, upon which this function is largely based}
26# construct and plot a dataframe of three 'traces', excluding the largest values
29trace.plot(obj=d, xlim=list(max=10), col=c("maroon","gray","purple"), alpha=0.3, lwd=2)
32# construct and plot a dataframe of two 'traces' using a log scale