View source: R/getBestShiftConfiguration.R
getBestShiftConfiguration | R Documentation |
BAMM
analysisGet the rate shift configuration with the maximum a
posteriori probability, e.g., the shift configuration that was sampled
most frequently with BAMM
.
getBestShiftConfiguration(x, expectedNumberOfShifts, threshold = 5)
x |
Either a |
expectedNumberOfShifts |
The expected number of shifts under the prior. |
threshold |
The marginal posterior-to-prior odds ratio used as a cutoff for distinguishing between "core" and "non-core" rate shifts. |
This function estimates the rate shift configuration with the
highest maximum a posteriori (MAP) probability. It returns a
bammdata
object with a single sample. This can be plotted with
plot.bammdata
, and individual rate shifts can then
be added with addBAMMshifts
.
The parameters of this object are averaged over all samples in the
posterior that were assignable to the MAP shift configuration. All
non-core shifts have been excluded, such that the only shift
information contained in the object is from the "significant" rate
shifts, as determined by the relevant marginal posterior-to-prior odds
ratio threshold
.
You can extract the same information from the credible set of shift
configurations. See credibleShiftSet
for more
information.
A class bammdata
object with a single sample, corresponding
to the diversification rate shift configuration with the maximum a
posteriori probability. See getEventData
for details.
Dan Rabosky
getEventData, credibleShiftSet, plot.credibleshiftset, plot.bammdata
data(whales, events.whales) ed <- getEventData(whales, events.whales, burnin=0.1, nsamples=500) # Get prior distribution on shifts-per-branch: bp <- getBranchShiftPriors(whales, expectedNumberOfShifts = 1) # Pass the event data object in to the function: best <- getBestShiftConfiguration(ed, expectedNumberOfShifts = 1, threshold = 5) plot(best, lwd=2) addBAMMshifts(best, cex=2) # Now we can also work with the credible shift set: css <- credibleShiftSet(ed, expectedNumberOfShifts = 1, threshold = 5) summary(css) # examine model-averaged shifts from MAP configuration- # This gives us parameters, times, and associated nodes # of each evolutionary rate regime (note that one of # them corresponds to the root) css$eventData[[1]]; # Get bammdata representation of MAP configuration: best <- getBestShiftConfiguration(css, expectedNumberOfShifts = 1, threshold = 5) plot(best) addBAMMshifts(best)
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