plotRootValue {ratematrix}

Plot posterior distribution of root values for the traits

Description

Plot the posterior distribution of root values sampled from the MCMC analysis or samples from the prior distribution.

Usage

plotRootValue(
  chain,
  color = "black",
  set.xlab = NULL,
  set.cex.lab = 1,
  set.cex.axis = 1.5,
  set.xlim = NULL,
  hpd = 100,
  mfrow = 1,
  vline.values = NULL,
  vline.color = NULL,
  vline.wd = NULL,
  show.zero = FALSE
)

Arguments

chain

the posterior distribution loaded from the files using 'readMCMC' or samples from the prior generated with the 'samplePrior' function.

color

the color for the histograms.

set.xlab

a vector with legends for the x axes. If 'NULL' (default), the names are 'trait_1' to 'trait_n".

set.cex.lab

the cex value for the labels (default is 1).

set.cex.axis

the cex value for the axes numbers (default is 1.5).

set.xlim

the xlim for the plot. Need to be a vector with the lower and higher bound.

hpd

the Highest Posterior Density interval to highlight in the plot. Parameter values outside this interval will be colored in white. A numeric value between 0 and 100 (default is 100).

mfrow

the number of rows to use in the figure (default is 1).

vline.values

numeric values for plotting vertical lines. Can be a single value recycled for each of the plots or a vector with length equal to the number of traits.

vline.color

character vector with colors for the vertical lines. Can be a single color if length of 'vline.values' is 1 otherwise need to have length equal to the number of traits.

vline.wd

numeric value for the width of the vertical lines. Can be a single value if length of 'vline.values' is 1 otherwise need to have length equal to the number of traits.

show.zero

whether a vertical line should be plotted showing the position of the value 0 in the plot.

Value

A plot with the posterior density of root values or distribution of root values sampled from the prior.

Author(s)

Daniel S. Caetano and Luke J. Harmon

Examples

data( centrarchidae )
dt.range <- t( apply( centrarchidae$data, 2, range ) )
## The step size for the root value can be set given the range we need to sample from:
w_mu <- ( dt.range[,2] - dt.range[,1] ) / 10
par.sd <- cbind(c(0,0), sqrt( c(10,10) ))
prior <- makePrior(r=2, p=2, den.mu="unif", par.mu=dt.range, den.sd="unif", par.sd=par.sd)
prior.samples <- samplePrior(n = 1000, prior = prior)
start.point <- samplePrior(n=1, prior=prior)
## Plot the prior. Red line shows the sample from the prior that will set the starting 
##      point for the MCMC.
plotRatematrix(prior.samples, point.matrix = start.point$matrix, point.color = "red"
               , point.wd = 2)
plotRootValue(prior.samples)
handle <- ratematrixMCMC(data=centrarchidae$data, phy=centrarchidae$phy.map, prior=prior
                         , gen=10000, w_mu=w_mu, dir=tempdir())
posterior <- readMCMC(handle, burn = 0.2, thin = 10)
## Again, here the red line shows the starting point of the MCMC.
plotRatematrix( posterior, point.matrix = start.point$matrix, point.color = "red"
               , point.wd = 2)
plotRootValue(posterior)