makeplot.splitfreqs.sliding: Plot split frequencies in sliding windows over the course of...

In rwty: R We There Yet? Visualizing MCMC Convergence in Phylogenetics

Description

Takes a list of rwty.chain objects. Plots the split frequencies of clades over the course of the MCMC, calculated from windows of a specified size. Only plots the n.clades most variable clades, as measured by the standard deviation of the split frequencies of each clade across the MCMC. Each line in the plot represents a single clade. The colour of the line represents the standard deviation of the split frequencies of that clade across the MCMC.

Usage

makeplot.splitfreqs.sliding(chains, burnin = 0, n.clades = 20,
  window.size = 20, facet = TRUE, rank = "ess")

Arguments

chains	A list of rwty.chain objects.
burnin	The number of trees to eliminate as burnin
n.clades	The number of clades to plot
window.size	The number of trees to include in each window (note, specified as a number of sampled trees, not a number of generations)
facet	(TRUE/FALSE). TRUE: return a single plot with one facet per chain; FALSE: return a list of individual plots with one plot per chain
rank	('ess', 'sd'). How to rank the clades? By default, we plot the 20 'worst' clades. This parameter sets the definition of 'worst'. The default is to rank the clades by increasing Effective Sample Size (i.e. the 20 worst clades are those with the lowest ESS), since in a sliding window plot we expect well-sampled splits to have a high value (rank = "ess"). The original AWTY ranked clades by their standard deviations. To do this, just set rank = 'sd'.

Value

splitfreqs.plot Either a single ggplot2 object or a list of ggplot2 objects.

Examples

## Not run: 
data(fungus)
makeplot.splitfreqs.sliding(fungus, burnin = 20, n.clades=25)

## End(Not run)

rwty documentation built on May 2, 2019, 4 p.m.