DOI: 10.5061/dryad.8mj66m5c
title: Data from: A comment on the use of stochastic character maps to estimate evolutionary rate variation in a continuously valued trait

description: Phylogenetic comparative biology has progressed considerably in recent
 years. One of the most important developments has been the application of
 likelihood-based methods to fit alternative models for trait evolution in
 a phylogenetic tree with branch lengths proportional to time. An important
 example of this type of method is O’Meara et al.’s (2006) “noncensored”
 test for variation in the evolutionary rate for a continuously valued
 character trait through time or across the branches of a phylogenetic
 tree. According to this method, we first hypothesize evolutionary rate
 regimes on the tree (called “painting” in Butler and King, 2004); and then
 we fit an evolutionary model, specifically the popular Brownian model, in
 which the instantaneous variance of the Brownian random diffusion process
 has different values in different parts of the phylogeny. The authors
 suggest that to test a hypothesis that the state of a discrete character
 influenced the rate of a continuous character, one could use the approach
 of Neilsen (2002) to first stochastically map the discretely valued trait,
 and then “test to see whether the portions of the tree with one state for
 the discrete character have a different rate of evolution for the
 continuous character than portions of the tree to which the other discrete
 state has been mapped” (O’Meara et al., 2006, p. 931). Indeed, this has
 become common practice for this and other closely related methods. Here, I
 examine this practice. In particular, I show that evolutionary rates
 estimated this way (i.e., by using maximum likelihood [ML] to fit a
 multirate model on each stochastically mapped tree; and then averaging
 across trees) are systematically biased to be more similar to each other
 than are the underlying generating parameters. My analysis also reveals
 that this effect is dependent on the rate of evolution for the discrete
 trait. Specifically, if the rate of evolution for the discrete character
 is low then the difference between the true history and any stochastically
 mapped 1 history is generally small. This results in evolutionary rates
 for the continuous trait that are estimated with little bias. Conversely,
 if the rate of evolution for the discrete character is very high, then the
 true and hypothesized character histories are often extremely dissimilar,
 evolutionary rate estimates are biased to be more similar to each other
 than their underlying generating values, and we lose power to distinguish
 evolutionary rates on the tree.
description: Simulation code and
 dataThis zipped tarball
 contains the following files to accompany my submitted manuscript: 1)
 combined.results.RData (combined results from all simulations; 2)
 Fig1.simulation.R (R code use for simulating the example of Figure 1); 3)
 Five files named according to the convention qX.Y.R - these contain the
 code used in simulation for each value of q=X.Y; 4) Five files named
 according to the convention qX.Y.RData - these contain the results from
 simulation for each value of q=X.Y; 5) summary.code.R (R code for
 summarizing the results from simulation).all.code.and.data.tar.gzRevised simulation code and dataThe zipped tarball contains revised simulation code and data for my revisions of 23 August 2012. 1) combined.results.RData (combined results from all simulations; 2) Fig1.simulation.R (R code use for simulating the example of Figure 1); 3) Five files named according to the convention qX.Y.R - these contain the code used in simulation for each value of q=X.Y; 4) Five files named according to the convention qX.Y.RData - these contain the results from simulation for each value of q=X.Y; 5) summary.code.R (R code for summarizing the results from simulation); 6) a folder comparison.to.SIMMAP containing comparison code in R, an .RData file with results, and the input (.xml) and output (.nex) files from SIMMAP.Revell.23Aug12.all.code.and.data.tar.gz