make.simmap | R Documentation |
Performs stochastic character mapping (Huelsenbeck et al., 2003) using several different alternative methods.
make.simmap(tree, x, model="SYM", nsim=1, ...)
simmap(object, ...)
tree |
a phylogenetic tree as an object of class |
x |
a vector containing the tip states for a discretely valued character, or a matrix containing the prior probabilities of tip states in rows and character states as column names. The names (if |
model |
a character string containing the model or a transition model specified in the form of a matrix. See |
nsim |
number of simulations. If |
... |
optional arguments. So far, |
object |
for generic |
For Q="empirical"
, make.simmap
first fits a continuous-time reversible Markov model for the evolution of x
and then simulates stochastic character histories using that model and the tip states on the tree. This is the same procedure that is described in Bollback (2006), except that simulation is performed using a fixed value of the transition matrix, Q, instead of by sampling Q from its posterior distribution.
For Q="mcmc"
, make.simmap
first samples Q nsim
times from the posterior probability distribution of Q using MCMC, then it simulates nsim
stochastic maps conditioned on each sampled value of Q.
For Q
set to a matrix, make.simmap
samples stochastic mappings conditioned on the fixed input matrix.
make.simmap
uses code that has been adapted from ape's function ace
(by Paradis et al.) to perform Felsenstein's pruning algorithm to compute the likelihood.
As of phytools >= 0.2-33 x
can be a vector of states or a matrix containing the prior probabilities of tip states in rows. In this case the column names of x
should contain the states, and the row names should contain the tip names.
Note that there was a small (but potentially significant) bug in how node states were simulated by make.simmap
in versions of phytools <= 0.2-26. Between phytools 0.2-26 and 0.2-36 there was also a bug for asymmetric models of character change (e.g., model="ARD"
). Finally, between phytools 0.2-33 and phytools 0.2-47 there was an error in use of the conditional likelihoods for the root node, which caused the root node of the tree to be sampled incorrectly. Giorgio Bianchini pointed out that in phytools 1.0-1 (and probably prior recent versions) there was an error sampling the state at the root node of the tree based on the input prior (pi
) supplied by a user – except for pi="equal"
(a flat prior, the default) or for a prior distribution in which one or another state was known to be the global root state (e.g., pi=c(1,0)
, pi=c(0,1)
, etc.). All of these issues should be fixed in the current and all later versions.
If tree
is an object of class "multiPhylo"
then nsim
stochastic maps are generated for each input tree.
A object of class "simmap"
or "multiSimmap"
which consists of an object of class "phylo"
(or a list of such objects with class "multiPhylo"
), with the following additional elements:
maps |
a list of named vectors containing the times spent in each state on each branch, in the order in which they occur. |
mapped.edge |
a matrix containing the total time spent in each state along each edge of the tree. |
Q |
the assumed or sampled value of |
logL |
the log-likelihood of the assumed or sampled |
Liam Revell liam.revell@umb.edu
Bollback, J. P. (2006) Stochastic character mapping of discrete traits on phylogenies. BMC Bioinformatics, 7, 88.
FitzJohn, R. G., W. P. Maddison, and S. P. Otto (2009) Estimating trait-dependent speciation and extinction rates from incompletely resolved phylogenies. Systematic Biology, 58, 595-611.
Huelsenbeck, J. P., R. Neilsen, and J. P. Bollback (2003) Stochastic mapping of morphological characters. Systematic Biology, 52, 131-138.
Paradis, E., J. Claude, and K. Strimmer (2004) APE: Analyses of phylogenetics and evolution in R language. Bioinformatics, 20, 289-290.
Revell, L. J. (2024) phytools 2.0: an updated R ecosystem for phylogenetic comparative methods (and other things). PeerJ, 12, e16505.
Revell, L. J. and L. J. Harmon (2022) Phylogenetic Comparative Methods in R. Princeton University Press.
brownie.lite
, brownieREML
, countSimmap
, describe.simmap
, evol.vcv
, plotSimmap
, read.simmap
, write.simmap
## Not run:
## load tree and data from Revell & Collar (2009)
data(sunfish.tree)
data(sunfish.data)
## extract discrete character (feeding mode)
fmode<-setNames(sunfish.data$feeding.mode,
rownames(sunfish.data))
## fit model
er_model<-fitMk(sunfish.tree,fmode,model="ER",
pi="fitzjohn")
## do stochastic mapping
sunfish_smap<-simmap(er_model)
## print a summary of the stochastic mapping
summary(sunfish_smap)
## plot a posterior probabilities of ancestral states
cols<-setNames(c("blue","red"),levels(fmode))
plot(summary(sunfish_smap),colors=cols,ftype="i")
legend("topleft",c("non-piscivorous","piscivorous"),
pch=21,pt.bg=cols,pt.cex=2)
par(mar=c(5.1,4.1,4.1,2.1),las=1)
## plot posterior density on the number of changes
plot(density(sunfish_smap),bty="l")
title(main="Posterior distribution of changes of each type",
font.main=3)
## End(Not run)
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