def add_prior(self, prior):
RelaxedClock.add_prior(self, prior)
gamma_param_prior = xml.prior(prior,
id="clockRateGammaShapePrior.s:%s" %
self.name,
name="distribution",
x="@clockRateGammaShape:%s" % self.name)
xml.Exponential(gamma_param_prior,
id="clockRateGammaShapePriorExponential.s:%s" %
self.name,
name="distr",
mean="1.0")
def add_prior(self, prior):
BaseClock.add_prior(self, prior)
# Gamma prior over rates
sub_prior = xml.prior(prior,
id="RandomRatesPrior.c:%s" % self.name,
name="distribution",
x="@clockrates.c:%s" % self.name)
xml.Gamma(sub_prior,
id="RandomRatesPrior:%s" % self.name,
name="distr",
alpha="@%s" % self.shape_id,
beta="@%s" % self.scale_id)
# Exponential prior over Gamma scale parameter
# (mean param copied from rate heterogeneity implementation in BaseModel)
if self.estimate_variance:
sub_prior = xml.prior(prior,
id="randomClockGammaScalePrior.s:%s" %
self.name,
name="distribution",
x="@%s" % self.shape_id)
xml.Exponential(sub_prior,
id="randomClockGammaScalePriorExponential.s:%s" %
self.name,
mean="0.23",
name="distr")
# Poisson prior over number of rate changes
sub_prior = xml.prior(prior,
id="RandomRateChangesPrior.c:%s" % self.name,
name="distribution")
xml.x(sub_prior,
id="RandomRateChangesCount:%s" % self.name,
spec="util.Sum",
arg="@Indicators.c:%s" % self.name)
poisson = xml.distr(sub_prior,
id="RandomRatechangesPoisson.c:%s" % self.name,
spec="beast.math.distributions.Poisson")
xml.parameter(poisson,
text="0.6931471805599453",
id="RandomRateChangesPoissonLambda:%s" % self.name,
estimate=False,
name="lambda")
def _add_prior(self, prior, name):
switch_prior = xml.prior(
prior,
id="%s:pdcovarion_s_prior.s" % name,
name="distribution",
x="@%s:pdcovarion_s.s" % name)
gamma = xml.Gamma(switch_prior, id="%s: Gamma.0" % name, name="distr")
xml.parameter(
gamma,
text="0.05",
id="%s:pdcovarion_switch_gamma_param1" % name,
name="alpha",
lower="0.0",
upper="0.0")
xml.parameter(
gamma,
text="10.0",
id="%s:pdcovarion_switch_gamma_param2" % name,
name="beta",
lower="0.0",
upper="0.0")
origin_prior = xml.prior(
prior,
id="%s:pdcovarion_origin_prior.s" % name,
name="distribution",
x="@%s:pdcovarion_origin.s" % name)
xml.Uniform(
origin_prior,
id="%s:PDCovOriginUniform" % name,
name="distr",
upper="Infinity")
death_prior = xml.prior(
prior,
id="%s:pdcovarion_death_prior.s" % name,
name="distribution",
x="@{:}:pdcovarion_death.s".format(name))
xml.Exponential(
death_prior,
id="%s:PDCovDeathExp" % name,
name="distr",
mean="1.0")
def add_prior(self, prior):
"""
Add prior distributions for Gamma-distributed rate heterogenetiy, if
configured.
"""
if self.rate_variation:
# Gamma prior with mean 1 over all mutation rates
sub_prior = xml.prior(prior,
id="featureClockRatePrior.s:%s" % self.name,
name="distribution")
compound = xml.input(sub_prior,
id="featureClockRateCompound:%s" % self.name,
spec="beast.core.parameter.CompoundValuable",
name="x")
plate = xml.plate(compound, var="rate", range=self.all_rates)
xml.var(plate, idref="featureClockRate:%s:$(rate)" % self.name)
xml.input(sub_prior,
id="featureClockRatePriorGamma:%s" % self.name,
spec="beast.math.distributions.Gamma",
name="distr",
alpha="@featureClockRateGammaShape:%s" % self.name,
beta="@featureClockRateGammaScale:%s" % self.name)
# Exponential hyperprior on scale of Gamma prior
# Exponential prior favours small scales over large scales, i.e. less rate variation
# Mean scale 0.23 chosen for general sensibility, e.g.:
# - Prior distribution is roughly 50/50 that ratio of fastest
# to slowest feature rate in a dataset of size 200 is below
# or above 10.
# - Prior probability of roughly 0.90 that this ratio is below
# 100.
sub_prior = xml.prior(
prior,
id="featureClockRateGammaScalePrior.s:%s" % self.name,
name="distribution",
x="@featureClockRateGammaScale:%s" % self.name)
xml.Exponential(
sub_prior,
id="featureClockRateGammaShapePriorExponential.s:%s" %
self.name,
mean="0.23",
name="distr")
def add_branchrate_model(self, beast):
RelaxedClock.add_branchrate_model(self, beast)
xml.Exponential(self.branchrate,
id="ExponentialDistribution.c:%s" % self.name,
mean=self.mean_rate_idref,
name="distr")