| Title: | An R package for BPP |
|---|---|
| Description: | Functions to work with the multi-species coalescent program BPP, for example, functions to calibrate BPP trees to geological time. |
| Authors: | Mario dos Reis |
| Maintainer: | Mario dos Reis <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.6.3 |
| Built: | 2024-12-01 07:41:29 UTC |
| Source: | https://github.com/dosreislab/bppr |
Calculate Bayes factors and posterior model probabilities
bayes.factors(..., prior = NULL, boot = TRUE, n = 1000, prob = 0.95)bayes.factors(..., prior = NULL, boot = TRUE, n = 1000, prob = 0.95)
... |
list of marginal likelihood objects, see details |
prior |
numeric, the prior model probabilities |
boot |
logical, whether to perform parametric boostrap of probabilities |
n |
numeric, number of bootstrap samples |
prob |
numeric, the probability used to calculate the boostrap CI |
Input is a list of marginal likelihood objects, with each object generated by
either stepping.stones() or gauss.quad(). If boot =
TRUE, parametric bootstrap is performed by assuming the log-marginal
likelihood estimates are normally distributed with standard deviation equal
to the standard error. The re-sampled n marginal log-likelihoods are
used to estimate re-sampled posterior probabilities, and to calculate an
equal-tail bootstrap confidence interval for these.
Note that the length of prior should be the same as the number of
models being compared. The prior is rescaled so that
sum(prior) == 1.
A list with elements bf, the Bayes factors; pr, the posterior
model probabilities; prior the prior model probabilities and, if
boot = TRUE, pr.ci the equal-tail bootstrap confidence interval.
Mario dos Reis
# See Table 5 in dos Reis et al. (2018, Syst. Biol., 67: 594-615) # Bayesian selection of relaxed clock models for the 1st and 2nd sites # of mitochondrial protein-coding genes of primates # Models: strick clock, independent-rates, and autocorrelated-rates sc <- list(); sc$logml <- -16519.03; sc$se <- .01 ir <- list(); ir$logml <- -16480.58; ir$se <- .063 ar <- list(); ar$logml <- -16477.82; ar$se <- .035 bayes.factors(sc, ir, ar) bayes.factors(sc, ir, ar, prior=c(.25,.5,.25)) bayes.factors(sc, ir, ar, prior=c(0,1,0))# See Table 5 in dos Reis et al. (2018, Syst. Biol., 67: 594-615) # Bayesian selection of relaxed clock models for the 1st and 2nd sites # of mitochondrial protein-coding genes of primates # Models: strick clock, independent-rates, and autocorrelated-rates sc <- list(); sc$logml <- -16519.03; sc$se <- .01 ir <- list(); ir$logml <- -16480.58; ir$se <- .063 ar <- list(); ar$logml <- -16477.82; ar$se <- .035 bayes.factors(sc, ir, ar) bayes.factors(sc, ir, ar, prior=c(.25,.5,.25)) bayes.factors(sc, ir, ar, prior=c(0,1,0))
Estimate marginal likelihood by thermodynamic integration and Gauss-Legendre
quadrature from a sample of n power posterior MCMC chains sampled
with mcmctree (or bpp).
gauss.quad(mcmcf = "mcmc.txt", betaf = "beta.txt")gauss.quad(mcmcf = "mcmc.txt", betaf = "beta.txt")
mcmcf |
character, mcmc output file name |
betaf |
character, file with beta values |
The MCMC samples should be stored in a directory structure created
by make.bfctlf with method = "gauss-quad". The function
will read the stored log-likelihood values and calculate the log-marginal
likelihood.
Numerical integration is done using Gauss-Legendre quadrature. See Rannala and Yang (2017) for details (also dos Reis et al. 2017, Appendix 2).
A list with components logml, the log-marginal likelihood estimate;
se, the standard error of the estimate; mean.logl, the mean of
log-likelihood values sampled for each beta; and b, the beta values
used.
Mario dos Reis
Rannala B and Yang Z. (2017) Efficient Bayesian species tree inference under the multispecies coalescent. Systematic Biology 66: 823-842.
dos Reis et al. (2017) Using phylogenomic data to explore the effects of relaxed clocks and calibration strategies on divergence time estimation: Primates as a test case. bioRxiv
make.bfctlf to prepare directories and mcmctree or bpp control
files to calculate the power posterior.
This dataset contains the results from the BPP A00 analysis of hominid evolution from Angelis and dos Reis (2015).
hominidshominids
hominids is a list with elements mcmc, a dataframe with
20,000 rows and 8 columns, and tree, an object of class
phylo from the ape package.
mcmc is a posterior sample from a BPP A00 MCMC analysis containing
the relative divergence times (tau's) and nucleotide diversities (theta's)
for the four species ape (hominid) phylogeny.
tree contains the phylogeny with node ages given as the posterior
means of the tau's in mcmc.
K. Angelis and M. dos Reis (2015) The impact of ancestral population size and incomplete lineage sorting on Bayesian estimation of species divergence times. Curr. Zool., 61: 874–885.
Make appropriate beta values
make.beta(n, method = c("step-stones", "gauss-quad"), a = 5)make.beta(n, method = c("step-stones", "gauss-quad"), a = 5)
n |
numeric, number of beta points |
method |
character, the method to choose the beta points, see details |
a |
numeric, exponent for stepping stones beta generation, see details |
If method = "step-stones", the beta values are given by the
formula
Values of a between 5 to 8 appear appropriate. Large a values
produce beta values close to zero.
If method = "gauss-quad", the beta values are calculated according to
the n Gauss-Legendre quadrature rule (see Rannala and Yang, 2017).
Numeric vector with n beta values
Mario dos Reis
Rannala B and Yang Z (2017) Efficient Bayesian species tree inference under the multispecies coalescent. Systematic Biology, 66: 823–842.
The generated beta values are suitable input for make.bfctlf.
Prepare mcmctree or bpp control files for marginal likelihood calculation
make.bfctlf(beta, ctlf = "bpp.ctl", betaf = "beta.txt")make.bfctlf(beta, ctlf = "bpp.ctl", betaf = "beta.txt")
beta |
numeric vector of beta values |
ctlf |
character, mcmctree or bpp control file template |
betaf |
character, file onto which to write selected beta values |
This function generates a set of n directories each
containing a modified ctlf control file with the appropriate beta
value to run mcmctree (or bpp) to obtain MCMC samples under the required
power-posterior distribution. For the general theory of marginal likelihood
calculation with power posteriors see Yang (2014).
The beta values are printed to betaf.
Mario dos Reis
Yang Z (2014) Molecular Evolution: A Statistical Approach. Oxford University Press. Pages 256–260.
Create an MCMC summary from a BPP or MCMCTree analysis
mcmc.summary(mcmc, prob = 0.95)mcmc.summary(mcmc, prob = 0.95)
mcmc |
Data frame with the MCMC output of BPP or MCMCTree |
prob |
Numeric, probability for credibility interval calculation |
mcmc should contain the output (say from file
mcmc.txt) generated by a BPP A00 or MCMCtree analysis. The function
will calculate the posterior (prior) means, the equal-tail credibility
interval (CI), and the highest posterior (prior) density (HPD) CI. prob is
used to calculate the CIs. For example, if prob = 95
95
A list with elements means, eq.ci, and hpd.ci
containing the posterior (or prior) means, equal tail CI and HPD CI.
Mario dos Reis
## Not run: mcmc.summary(hominids$mcmc[,-1]) ## End(Not run)## Not run: mcmc.summary(hominids$mcmc[,-1]) ## End(Not run)
Plot a densi-tree from an MCMC sample from a BPP or MCMCTree analysis
mcmc2densitree( tree, mcmc, time.name, thin, col = "blue", alpha = 1, y.offset = 0, pfract = 0.1, plot.labels = TRUE, axis = TRUE, add = FALSE, tip.ages = NULL )mcmc2densitree( tree, mcmc, time.name, thin, col = "blue", alpha = 1, y.offset = 0, pfract = 0.1, plot.labels = TRUE, axis = TRUE, add = FALSE, tip.ages = NULL )
tree |
an object of class phylo. |
mcmc |
data frame with an MCMC sample from MCMCTree or a BPP A00 analysis. |
time.name |
character vector of length one. |
thin |
numeric, the fraction of MCMC samples to keep. |
col |
character, the color for branches. |
alpha |
numeric, between 0 and 1, the branch color transparency. |
y.offset |
numeric, the vertical offset for plotting the tree. |
pfract |
numeric, how much of the plotting space to used for plotting
the tip labels. If |
plot.labels |
logical, whether to plot the tip labels. Ignored if
|
axis |
logical, whether to plot the x axis. |
add |
logical, if TRUE add the trees to an existing plot, otherwise create a new plot. |
tip.ages |
numeric, the ages of the tips, with the most recent tip
having age zero, and the oldest tip having the largest age. If |
The function will reduce the MCMC sample to dim(mcmc)[1] *
thin observations. Then the node ages in each observarion are used to plot
each tree in the sample. For a tree with s species. The y
coordinates of the tips are given by 0:(s - 1) + y.offset.
The tree must be rooted, strictly bifurcating, and be the same tree
used to genarate the BPP (A00) or MCMCTree MCMC samples.
Mario dos Reis
data(microcebus) mcmc2densitree(microcebus$tree, microcebus$mcmc, time.name="tau_", thin=0.05, alpha=0.01, col="blue") title(xlab="Distance (substitutions per site)") data(hominids) # Calibrate the hominid phylogeny with a uniform fossil calibration of # between 6.5 to 10 Ma for the human-chimp divergence, and plot the # calibrated sample calmsc <- msc2time.t(mcmc=hominids$mcmc, node="7humanchimp", calf=runif, min=6.5, max=10) mcmc2densitree(hominids$tree, calmsc, "t_", thin=0.05, alpha=0.01) title(xlab="Divergence time (Ma)")data(microcebus) mcmc2densitree(microcebus$tree, microcebus$mcmc, time.name="tau_", thin=0.05, alpha=0.01, col="blue") title(xlab="Distance (substitutions per site)") data(hominids) # Calibrate the hominid phylogeny with a uniform fossil calibration of # between 6.5 to 10 Ma for the human-chimp divergence, and plot the # calibrated sample calmsc <- msc2time.t(mcmc=hominids$mcmc, node="7humanchimp", calf=runif, min=6.5, max=10) mcmc2densitree(hominids$tree, calmsc, "t_", thin=0.05, alpha=0.01) title(xlab="Divergence time (Ma)")
Convert an MCMC sample from BPP or MCMCTree to a list of trees
mcmc2multiphylo(tree, mcmc, time.name, thin)mcmc2multiphylo(tree, mcmc, time.name, thin)
tree |
an object of class phylo |
mcmc |
data frame with an MCMC sample from MCMCTree or a BPP A00 analysis |
time.name |
character vector of length one |
thin |
numeric, the fraction of MCMC samples to keep |
tree must be rooted and strictly bifurcating, and it must
match the tree used by BPP or MCMCTree to obtain the MCMC sample. The
function uses the node ages in mcmc to calculate branch lengths and
generate a list of trees (with the same topology as tree), one tree
per (thinned) MCMC sample. The tips of the phylogeny are assumed to have
age zero.
An object of class multiPhylo (i.e., a list of trees).
Mario dos Reis
data(microcebus) # convert a BPP A00 MCMC sample of Microcebus spp. to a list of trees mtts <- mcmc2multiphylo(microcebus$tree, microcebus$mcmc, "tau_", thin=0.01) length(mtts) data(hominids) # Calibrate the hominid phylogeny with a uniform fossil calibration of # between 6.5 to 10 Ma for the human-chimp divergence. calmsc <- msc2time.t(mcmc=hominids$mcmc, node="7humanchimp", calf=runif, min=6.5, max=10) # convert the time-calibrated MCMC sample to a list of trees htts <- mcmc2multiphylo(hominids$tree, calmsc, "t_", thin=0.01) htts[[1]] ## Not run: # If you have the ape package installed, you can output the trees in Newick ape::write.tree(htts[1:5]) # The trees are suitable for plotting with the phangorn package # Relative node ages (tau's): mcon <- microcebus$tree$tip.label phangorn::densiTree(mtts, col="blue", alpha=0.04, cons=mcon, label.offset=.01) # Absolute node ages (in millions of years): hcon <- hominids$tree$tip.label phangorn::densiTree(htts, col="blue", alpha=0.04, cons=hcon, label.offset=.01) ## End(Not run)data(microcebus) # convert a BPP A00 MCMC sample of Microcebus spp. to a list of trees mtts <- mcmc2multiphylo(microcebus$tree, microcebus$mcmc, "tau_", thin=0.01) length(mtts) data(hominids) # Calibrate the hominid phylogeny with a uniform fossil calibration of # between 6.5 to 10 Ma for the human-chimp divergence. calmsc <- msc2time.t(mcmc=hominids$mcmc, node="7humanchimp", calf=runif, min=6.5, max=10) # convert the time-calibrated MCMC sample to a list of trees htts <- mcmc2multiphylo(hominids$tree, calmsc, "t_", thin=0.01) htts[[1]] ## Not run: # If you have the ape package installed, you can output the trees in Newick ape::write.tree(htts[1:5]) # The trees are suitable for plotting with the phangorn package # Relative node ages (tau's): mcon <- microcebus$tree$tip.label phangorn::densiTree(mtts, col="blue", alpha=0.04, cons=mcon, label.offset=.01) # Absolute node ages (in millions of years): hcon <- hominids$tree$tip.label phangorn::densiTree(htts, col="blue", alpha=0.04, cons=hcon, label.offset=.01) ## End(Not run)
This dataset contains the results from the BPP A00 analysis of mouse lemur evolution in Madagascar from Yoder et al. (2016).
microcebusmicrocebus
microcebus is a list with elements mcmc, a dataframe
with 20,000 rows and 12 columns, and tree, an object of class
phylo from the ape package.
mcmc is a posterior sample from a BPP A00 MCMC analysis containing
the relative divergence times (tau's) and nucleotide diversities (theta's)
for the six species mouse lemur (Microcebus spp) phylogeny.
tree contains the phylogeny with node ages given as the posterior
means of the tau's in mcmc.
A. D. Yoder, C. R. Campbell, M. B. Blanco, M. dos Reis, J. U. Ganzhorn, S. M. Goodman, K. E. Hunnicutt, P. A. Larsen, P. M. Kappeler, R. M. Rasoloarison, J. M. Ralison, D. L. Swofford, and D. W. Weisrock. (2016) Geogenetic patterns in mouse lemurs (genus Microcebus) reveal the ghosts of Madagascar's forests past. Proc. Nat. Acad. Sci. USA., 113: 8049–8056.
Calibrate a BPP A00 MCMC sample from a multi-species coalescent phylogeny to absolute time using a fossil calibration or a prior on the molecular rate.
msc2time.t(mcmc, node.name, calf, ...) msc2time.r(mcmc, u.mean, u.sd, g.mean, g.sd)msc2time.t(mcmc, node.name, calf, ...) msc2time.r(mcmc, u.mean, u.sd, g.mean, g.sd)
mcmc |
A data frame containing the MCMC output of a BPP A00 analysis |
node.name |
A character vector of length one with the name of the node to which the calibration will be applied |
calf |
A calibration function to generate random numbers |
... |
Further parameters passed to |
u.mean |
Numeric vector of length one with the mean for the per-generation molecular rate calibration |
u.sd |
Numeric vector of length one with the SD for for the per-generation molecular rate calibration |
g.mean |
Numeric vector of length one with the mean for the generation time calibration |
g.sd |
Numeric vector of length one with the SD for the generation time calibration |
msc2time.t will calibarte a BPP A00 analysis to geological
time using a fossil calibration and msc2time.r will do the same but
using a prior on the rate.
msc2time.t will obtain a sample of times from the random
distribution in calf. Suitable choices for calf are
runif, rgamma, and rslnorm. The sampled times are then
used to calculate the molecular rate, and then re-scale the relative times
(tau's) for the other nodes in mcmc to geological time.
u.mean and u.sd, and g.mean and g.sd, are used
to construct gamma density calibrations for the per-generation molecular
rate and generation time respectively. The gamma density with mean
and s.d. has shape and rate .
In msc2time.r, the gamma densities are used to obtain random samples
of the per-generation rate and generation time. From these the molecular
rate per absolute time unit is calculated, and then used to convert the
relative times (tau's) to absolute divergence times. The relative
population sizes (theta's) are converted to effective population sizes in
number of individuals.
Angelis and dos Reis (2015) give the random sampling procedure used in these functions. Yoder et al. (2016) gives an example of calibrating a mouse lemur phylogeny using a prior on the rate. The BPP A00 analysis is described in Yang (2015).
A data frame with a posterior sample of the calibrated times and
molecular rate, and additionally, in the case of msc2time.r, the
population sizes.
Mario dos Reis
K. Angelis and M. dos Reis (2015) The impact of ancestral population size and incomplete lineage sorting on Bayesian estimation of species divergence times. Curr. Zool., 61: 874–885.
Z. Yang (2015) The BPP program for species tree estimation and species delimitation. Curr. Zool., 61: 854–865.
A. D. Yoder, C. R. Campbell, M. B. Blanco, M. dos Reis, J. U. Ganzhorn, S. M. Goodman, K. E. Hunnicutt, P. A. Larsen, P. M. Kappeler, R. M. Rasoloarison, J. M. Ralison, D. L. Swofford, and D. W. Weisrock. (2016) Geogenetic patterns in mouse lemurs (genus Microcebus) reveal the ghosts of Madagascar's forests past. Proc. Nat. Acad. Sci. USA., 113: 8049–8056.
data(hominids) # Calibrate the hominid phylogeny with a uniform fossil calibration of # between 6.5 to 10 Ma for the human-chimp divergence. calmsc <- msc2time.t(mcmc=hominids$mcmc, node="7humanchimp", calf=runif, min=6.5, max=10) # posterior age of human-chimp (this is the same as the calibration) plot(density(calmsc$t_7humanchimp, adj=.1), xlab="Time (Ma)", main="Human-chimp age") rug(calmsc$t_7humanchimp) ## Not run: # calculate posterior summary (requires CODA package) mcmc.summary(calmsc) ## End(Not run) # Calibrate the hominid phylogeny using a shifted-lognormal fossil calibration # with minimum at 6.5 Ma for the human-chimp divergence. calmsc <- msc2time.t(mcmc=hominids$mcmc, node="7humanchimp", calf=rslnorm, shift=6.5, sdlog=.5) plot(density(calmsc$t_7humanchimp, adj=.1), xlab="Time (Ma)", main="Human-chimp age") rug(calmsc$t_7humanchimp) data(microcebus) # Calibrate the Microcebus phylogeny to absoluate divergence times using a # prior on the per-generation rate and generation time calmsc <- msc2time.r(mcmc=microcebus$mcmc, u.mean=8.7e-9, u.sd=1.65e-9, g.mean=3.75, g.sd=0.375) # posterior age of the phylogeny's root (in thousans of years) plot(density(calmsc$t_7OLMXRB / 1e3, adj=.1), xlab="Time (Ka)", main="Root age (Microcebus)") rug(calmsc$t_7OLMXRB / 1e3) # Posterior of the ancestral effective population at the root (in # thousands of individuals) plot(density(calmsc$Ne_7OLMXRB / 1e3, adj=.1), xlab="Ne (x 10^3 individuals)", main = "Ne at root (Microcebus)") rug(calmsc$Ne_7OLMXRB / 1e3)data(hominids) # Calibrate the hominid phylogeny with a uniform fossil calibration of # between 6.5 to 10 Ma for the human-chimp divergence. calmsc <- msc2time.t(mcmc=hominids$mcmc, node="7humanchimp", calf=runif, min=6.5, max=10) # posterior age of human-chimp (this is the same as the calibration) plot(density(calmsc$t_7humanchimp, adj=.1), xlab="Time (Ma)", main="Human-chimp age") rug(calmsc$t_7humanchimp) ## Not run: # calculate posterior summary (requires CODA package) mcmc.summary(calmsc) ## End(Not run) # Calibrate the hominid phylogeny using a shifted-lognormal fossil calibration # with minimum at 6.5 Ma for the human-chimp divergence. calmsc <- msc2time.t(mcmc=hominids$mcmc, node="7humanchimp", calf=rslnorm, shift=6.5, sdlog=.5) plot(density(calmsc$t_7humanchimp, adj=.1), xlab="Time (Ma)", main="Human-chimp age") rug(calmsc$t_7humanchimp) data(microcebus) # Calibrate the Microcebus phylogeny to absoluate divergence times using a # prior on the per-generation rate and generation time calmsc <- msc2time.r(mcmc=microcebus$mcmc, u.mean=8.7e-9, u.sd=1.65e-9, g.mean=3.75, g.sd=0.375) # posterior age of the phylogeny's root (in thousans of years) plot(density(calmsc$t_7OLMXRB / 1e3, adj=.1), xlab="Time (Ka)", main="Root age (Microcebus)") rug(calmsc$t_7OLMXRB / 1e3) # Posterior of the ancestral effective population at the root (in # thousands of individuals) plot(density(calmsc$Ne_7OLMXRB / 1e3, adj=.1), xlab="Ne (x 10^3 individuals)", main = "Ne at root (Microcebus)") rug(calmsc$Ne_7OLMXRB / 1e3)
Density, distribution and quantile functions, and random number generation for the shifted log-normal distribution.
dslnorm(x, shift, meanlog = 0, sdlog = 1, log = FALSE) pslnorm(q, shift, meanlog = 0, sdlog = 1, lower.tail = TRUE, log.p = FALSE) qslnorm(p, shift, meanlog = 0, sdlog = 1, lower.tail = TRUE, log.p = FALSE) rslnorm(n, shift, meanlog = 0, sdlog = 1)dslnorm(x, shift, meanlog = 0, sdlog = 1, log = FALSE) pslnorm(q, shift, meanlog = 0, sdlog = 1, lower.tail = TRUE, log.p = FALSE) qslnorm(p, shift, meanlog = 0, sdlog = 1, lower.tail = TRUE, log.p = FALSE) rslnorm(n, shift, meanlog = 0, sdlog = 1)
x, q
|
vector of quantiles. |
shift |
vector of shifts. |
meanlog, sdlog
|
mean and standard deviation of the distribution on the log scale with default values of 0 and 1 respectively. |
log, log.p
|
logical; if TRUE, probabilities p are given as log(p). |
lower.tail |
logical; if TRUE (default), probabilities are |
p |
vector of probabilities. |
n |
number of observations. If |
Let have a log-normal distribution with parameters
(meanlog) and (sdlog). Then has
a shifted log-normal distribution with shift (shift), mean
and variance .
Note [dpqr]slnorm are wrappers for the corresponding
[dpqr]lnorm functions.
dslnorm gives the density, pslnorm gives the distribution
function, qslnorm gives the quantile function, and rslnorm
generates random deviates.
The length of the result is determined by n for rlnorm, and
is the maximum of the lengths of the numerical arguments for the other
functions.
The numerical arguments other than n are recycled to the length of
the result. Only the first elements of the logical arguments are used.
curve(dslnorm(x, shift=6.5), from=0, to=15, n=1e3) rr <- rslnorm(1e3, shift=6.5) lines(density(rr, adj=.1), lty=2) all.equal (qslnorm(c(.025, .9), shift=6.5) - 6.5, qlnorm(c(.025, .9))) all.equal (pslnorm(10, shift=6.5), plnorm(10 - 6.5))curve(dslnorm(x, shift=6.5), from=0, to=15, n=1e3) rr <- rslnorm(1e3, shift=6.5) lines(density(rr, adj=.1), lty=2) all.equal (qslnorm(c(.025, .9), shift=6.5) - 6.5, qlnorm(c(.025, .9))) all.equal (pslnorm(10, shift=6.5), plnorm(10 - 6.5))
Estimate the marginal likelihood using the stepping stones
method from a sample of n power posterior MCMC chains sampled
with mcmctree (or bpp).
stepping.stones(mcmcf = "mcmc.txt", betaf = "beta.txt")stepping.stones(mcmcf = "mcmc.txt", betaf = "beta.txt")
mcmcf |
character, mcmc output file name |
betaf |
character, file with beta values |
The MCMC samples should be stored in a directory structure created
by make.bfctlf with method = "step-stones". The function will
read the stored log-likelihood values and calculate the log-marginal
likelihood.
An approximation based on the Delta method is used to calculate the standard error (see Xie et al. 2011). Warnings are given if the approximation appears unreliable.
A list with components logml, the log-marginal likelihood estimate;
se, the standard error of the estimate; mean.logl, the mean of
log-likelihood values sampled for each beta; and b, the beta values
used.
Mario dos Reis
Xie et al. (2011) Improving marginal likelihood estimation for Bayesian phylogenetic model selection. Systematic Biology, 60: 150–160.
make.bfctlf to prepare directories and mcmctree or bpp control
files to calculate the power posterior.