Usage
tess.analysis( tree,
initialSpeciationRate,
initialExtinctionRate,
empiricalHyperPriors = TRUE,
empiricalHyperPriorInflation = 10.0,
empiricalHyperPriorForm = c("lognormal","normal","gamma"),
speciationRatePriorMean = 0.0,
speciationRatePriorStDev = 1.0,
extinctionRatePriorMean = 0.0,
extinctionRatePriorStDev = 1.0,
initialSpeciationRateChangeTime = c(),
initialExtinctionRateChangeTime = c(),
estimateNumberRateChanges = TRUE,
numExpectedRateChanges = 2,
samplingProbability = 1,
missingSpecies = c(),
timesMissingSpecies = c(),
tInitialMassExtinction = c(),
pInitialMassExtinction = c(),
pMassExtinctionPriorShape1 = 5,
pMassExtinctionPriorShape2 = 95,
estimateMassExtinctionTimes = TRUE,
numExpectedMassExtinctions = 2,
estimateNumberMassExtinctions = TRUE,
MRCA = TRUE,
CONDITION = "survival",
BURNIN = 10000,
MAX_ITERATIONS = 200000,
THINNING = 100,
OPTIMIZATION_FREQUENCY = 500,
CONVERGENCE_FREQUENCY = 1000,
MAX_TIME = Inf, MIN_ESS = 500,
ADAPTIVE = TRUE,
dir = "" ,
priorOnly = FALSE,
verbose = TRUE)
Arguments
tree
The tree in 'phylo' format.
initialSpeciationRate
The initial value of the speciation rate when the MCMC is started. This can either be a single number of a vector of rates per interval.
initialExtinctionRate
The initial value of the extinction rate when the MCMC is started. This can either be a single number of a vector of rates per interval.
empiricalHyperPriors
Should we estimate the hyper-parameters empirically?
empiricalHyperPriorInflation
The scaling factor of the variance for the empirical hyperpriors.
empiricalHyperPriorForm
The possible empirical hyper prior distributions; either lognormal, normal or gamma
speciationRatePriorMean
The mean of the log-normal prior distribution for the speciation rate.
speciationRatePriorStDev
The standard deviation of the log-normal prior distribution for the speciation rate.
extinctionRatePriorMean
The mean of the log-normal prior distribution for the extinction rate.
extinctionRatePriorStDev
The standard deviation of the log-normal prior distribution for the extinction rate.
initialSpeciationRateChangeTime
The initial value of the time points when speciation rate-shifts occur. The number of time-shifts needs to be one smaller than the number of initial speciation rates.
initialExtinctionRateChangeTime
The initial value of the time points when extinction rate-shifts occur. The number of time-shifts needs to be one smaller than the number of initial extinction rates.
estimateNumberRateChanges
Do we estimate the number of rate shifts? Default is true.
numExpectedRateChanges
Expected number of rate changes which follow a Poisson process. The default gives 0.5 probability on 0 shifts.
samplingProbability
The extant taxa sampling probability at the present time.
missingSpecies
The number of species missed which originated in a given time interval (empirical taxon sampling).
timesMissingSpecies
The times intervals of the missing species (empirical taxon sampling).
tInitialMassExtinction
The initial value of the vector of times of the mass-extinction events. This is used as initial values for the MCMC.
pInitialMassExtinction
The initial value of the vector of survival probabilities of the mass-extinction events. This is used as initial values for the MCMC.
pMassExtinctionPriorShape1
The alpha (first shape) parameter of the Beta prior distribution for the survival probability of a mass-extinction event.
pMassExtinctionPriorShape2
The beta (second shape) parameter of the Beta prior distribution for the survival probability of a mass-extinction event.
estimateMassExtinctionTimes
Do we estimate the times of mass-extinction events? Default is true.
numExpectedMassExtinctions
Expected number of mass-extinction events which follow a Poisson process. The default gives 0.5 probability on 0 events.
estimateNumberMassExtinctions
Do we estimate the number of mass-extinction events? Default is true.
MRCA
Does the process start with the most recent common ancestor? If not, the tree must have a root edge!
CONDITION
do we condition the process on time|survival|taxa?
BURNIN
The length of the burnin period.
MAX_ITERATIONS
The maximum number of iteration of the MCMC. The default is 200000.
THINNING
The frequency how often samples are recorded during the MCMC. The default is every 100 iterations.
OPTIMIZATION_FREQUENCY
The frequency how often the MCMC moves are optimized. The default is every 500 iterations.
CONVERGENCE_FREQUENCY
The frequency how often we check for convergence? The default is every 1000 iterations.
MAX_TIME
The maximum time the MCMC is allowed to run in seconds. The default is Inf
MIN_ESS
The minimum number of effective samples (ESS) to assume convergence. The default is 500
ADAPTIVE
Do we use auto-tuning of the MCMC moves? The default is TRUE (recommended).
dir
The subdirectory in which the output will be stored. The default is the present directoy ("")
priorOnly
Do we sample from the prior only? The default is FALSE
verbose
Do you want detailed output?