Set priors for BAMM analysis.
setBAMMpriors(
phy,
total.taxa = NULL,
traits = NULL,
outfile = "myPriors.txt",
Nmax = 1000,
suppressWarning = FALSE
)The function does not return anything. It simply performs some calculations and writes formatted output to a file. However, if
outfile = NULL, then a named vector is returned.
An object of class phylo, e.g., the phylogenetic tree
you will analyze with BAMM.
If doing speciation-extinction analysis, the total
number of taxa in your clade. If your tree contains all taxa in the
clade (100% sampling), then leave this as NULL.
A filename where the trait data (BAMM format) are
stored, or a numeric vector named according to the tips in phy.
Leave NULL if doing a speciation-extinction analysis.
Filename for outputting the sample priors block. If
NULL, then a vector is returned instead.
If analyzing a very large tree for phenotypic evolution, uses only this many taxa to estimate priors for your dataset. Avoid matrix inversion issues with large numbers of tips.
Logical. If TRUE, then the warning about
setting the Poisson rate prior is suppressed. Only applies if
outfile = NULL.
Dan Rabosky
This is a "quick and dirty" tool for identifying approximately
acceptable priors for a BAMM analysis. We have found that
choice of prior can have a substantial impact on BAMM analyses.
It is difficult to simply set a default prior that applies across
datasets, because users often have trees with branch lengths in very
different units (e.g., numbers of substitutions versus millions of
years). Hence, without some careful attention, you can inadvertently
specify some very bad prior distributions. This function is designed
to at least put you in the right ballpark for decent prior
distributions, but there are no guarantees that these are most
appropriate for your data.
The general rules applied here are:
For the lambdaInitPrior, we estimate the speciation rate of the
data under a pure birth model. We then set this prior to give an
exponential distribution with a mean five times greater than this
computed pure birth speciation estimate.
The lambdaShiftPrior is the standard deviation of the normal
prior on the exponential change parameter k. We set the prior
distribution based on the age of the root of the tree. We set the
standard deviation of this distribution such that 2 standard
deviations give a parameter that will yield a 90% decline in the
initial speciation rate between the root of the tree and the tips.
The basic model is lambda(t) = lambda_0 * exp(k * t). This is a
straightforward calculation: let x = -log(0.1) / TMAX, where TMAX is
the age of the tree. Then set the standard deviation equal to (x / 2).
We set muInitPrior equal to lambdaInitPrior.
For trait evolution, we first compute the maximum likelihood estimate
of the variance of a Brownian motion process of trait evolution. The
prior betaInitPrior is then set to an exponential distribution
with a mean 5 times greater than this value (similar to what is done
for lambda and mu, above).
This function generates an output file containing a prior parameters
block that can be pasted directly into the priors section of your
BAMM control file.
# for diversification analyses
data(whales)
setBAMMpriors(phy = whales, total.taxa = 89, outfile = NULL)
# for trait analyses
data("primates")
data("mass.primates")
## create a named vector of trait values
mass <- setNames(mass.primates[,2], mass.primates[,1])
setBAMMpriors(phy = primates, traits = mass, outfile = NULL)
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