doRun: Run Approximate Bayesian Computation for Phylogenetic...
In bomeara/treevo: Using ABC to Understand Trait Evolution
doRun R Documentation
Run Approximate Bayesian Computation for Phylogenetic Comparative Methods
Description
The doRun functions are the main interface for
TreEvo users to do Approximate Bayesian Computation (ABC) analysis,
effectively wrapping the simulateWithPriors functions
to perform simulations, which themselves are wrappers for
the doSimulation function. The two current doRun
functions are doRun_prc which applies a partial
rejection control (PRC) ABC analysis over multiple generations of simulations,
and doRun_rej which performs a full rejection ('rej') ABC analysis.
Usage
doRun_prc(
phy,
traits,
intrinsicFn,
extrinsicFn,
startingPriorsValues,
startingPriorsFns,
intrinsicPriorsValues,
intrinsicPriorsFns,
extrinsicPriorsValues,
extrinsicPriorsFns,
numParticles = 300,
nStepsPRC = 5,
nRuns = 2,
nInitialSims = NULL,
nInitialSimsPerParam = 100,
generation.time = 1000,
TreeYears = max(branching.times(phy)) * 1e+06,
multicore = FALSE,
coreLimit = NA,
multicoreSuppress = FALSE,
standardDevFactor = 0.2,
epsilonProportion = 0.7,
epsilonMultiplier = 0.7,
validation = "CV",
scale = TRUE,
variance.cutoff = 95,
stopRule = FALSE,
stopValue = 0.05,
maxAttempts = Inf,
diagnosticPRCmode = FALSE,
jobName = NA,
saveData = FALSE,
verboseParticles = TRUE
)
doRun_rej(
phy,
traits,
intrinsicFn,
extrinsicFn,
startingPriorsValues,
startingPriorsFns,
intrinsicPriorsValues,
intrinsicPriorsFns,
extrinsicPriorsValues,
extrinsicPriorsFns,
generation.time = 1000,
TreeYears = max(branching.times(phy)) * 1e+06,
multicore = FALSE,
coreLimit = NA,
validation = "CV",
scale = TRUE,
nInitialSims = NULL,
nInitialSimsPerParam = 100,
variance.cutoff = 95,
standardDevFactor = 0.2,
jobName = NA,
abcTolerance = 0.1,
checkpointFile = NULL,
checkpointFreq = 24,
savesims = FALSE
)
Arguments
phy
A phylogenetic tree, in package ape's phylo format.
traits
Data matrix with rownames identical to phy@tip.label.
If a vector, traits will be coerced to a matrix, with element names as rownames.
intrinsicFn
Name of (previously-defined) function that governs how
traits evolve within a lineage, regardless of the trait values of other taxa.
extrinsicFn
Name of (previously-defined) function that governs how
traits evolve within a lineage, based on their own
('internal') trait vlaue and the trait values
of other taxa.
startingPriorsValues
A list of the same length as
the number of prior distributions specified in
startingPriorsFns (for starting values, this should
be one prior function specified for each trait
- thus one for most univariate trait analyses), with each
element of the list a vector the same length
as the appropriate number of parameters for that prior distribution
(1 for "fixed", 2 for "uniform", 2 for "normal",
2 for "lognormal", 2 for "gamma", 1 for "exponential").
startingPriorsFns
Vector containing names of prior distributions to
use for root states: can be one of
"fixed", "uniform", "normal",
"lognormal", "gamma", "exponential".
intrinsicPriorsValues
A list of the same length
as the number of prior distributions specified
in intrinsicPriorsFns (one prior function specified
for each parameter in the intrinsic model),
with each element of the list a vector the same length#
as the appropriate number of parameters for that prior distribution
(1 for "fixed", 2 for "uniform", 2 for "normal",
2 for "lognormal", 2 for "gamma", 1 for "exponential").
intrinsicPriorsFns
Vector containing names of prior distributions to
use for intrinsic function parameters: can be one of
"fixed", "uniform", "normal",
"lognormal", "gamma", "exponential".
extrinsicPriorsValues
A list of the same length as
the number of prior distributions specified in extrinsicPriorsFns
(one prior function specified for each parameter in the extrinsic model),
with each element of the list a vector the same length as the appropriate
number of parameters for that prior distribution
(1 for "fixed", 2 for "uniform", 2 for "normal",
2 for "lognormal", 2 for "gamma", 1 for "exponential").
extrinsicPriorsFns
Vector containing names of prior distributions to
use for extrinsic function parameters: can be one of
"fixed", "uniform", "normal",
"lognormal", "gamma", "exponential".
numParticles
Number of accepted particles per PRC generation.
nStepsPRC
Number of PRC generations to run.
nRuns
Number of independent PRC runs to be performed,
each consisting of independent sets of
initial simulations and PRC generations. Note that runs are
run sequentially, and not in parallel,
as the generation of particles within each run
makes use of the multicore functionality.
If nRuns is greater than 1, the output from
doRun_prc will be a list object composed of
multiple output lists, as described.
nInitialSims
Number of initial simulations used to calibrate particle rejection control algorithm.
If not given, this will be a function of the number of freely varying parameters.
nInitialSimsPerParam
If nInitialSims
is not given by the user, the number of initial simulations
performed to calibrate the particle rejection algorithm
will instead be the number of free parameters
in the model multiplied by nInitialSimsPerParam.
generation.time
The number of years per generation.
This sets the coarseness of the simulation; if it's set to 1000,
for example, the population's trait values change every 1000
calendar years. Note that this is in calendar years (see description
for argument TreeYears), and not in millions of years
(as is typical for dated trees in macroevolutionary studies).
Thus, if a branch is 1 million-year time-unit long, and a
user applies the default generation.time = 1000,
then 1000 evolutionary changes will be simulated along that branch.
See documentation for doSimulation for further details.
TreeYears
The amount of calendar time from the root
to the furthest tip. Most trees in macroevolutionary studies are dated with
branch lengths in units of millions of years, and thus the
default for this argument is max(branching.times(phy)) * 1e6.
If your tree has the most recent tip at time zero (i.e.,
the modern day), this would be the same as the root age of the tree. If your
branch lengths are not in millions of years, you should
alter this argument. Otherwise, leave this argument alone.
See documentation for doSimulation for further details.
multicore
Whether to use multicore, default is FALSE.
If TRUE, one of two suggested packages must be installed,
either doMC (for UNIX systems) or
doParallel (for Windows), which are used to activate multithreading.
If neither package is installed, this function will fail if multicore = TRUE.
coreLimit
Maximum number of cores to be used.
multicoreSuppress
This argument suppresses the
multicore code and will apply a plain vanilla serial for() loop instead
of doPar. Mainly to be used for developer diagnostic purposes.
standardDevFactor
Standard deviation for mutating states each time a new particle is generated in a PRC generation.
epsilonProportion
Sets tolerance for initial simulations.
epsilonMultiplier
Sets tolerance on subsequent PRC generations.
validation
Character argument controlling what
validation procedure is used by plsr.
Default is "CV" for cross-validation.
scale
This argument is passed to plsr.
It may be a numeric vector, or logical. If numeric vector,
the input is scaled by dividing each variable with the corresponding element of scale.
If scale = TRUE, the inpus is scaled by dividing
each variable by its sample standard deviation.
If cross-validation is selected (the default for returnPLSModel),
scaling by the standard deviation is done for every segment.
variance.cutoff
Minimum threshold percentage of variance explained for the
number of components included in the final PLS model fit. This value is a
percentage and must be between 0 and 100. Default is 95 percent.
stopRule
If TRUE, an analysis will be terminated prior to set number
of generations if the ratio of all parameters from one generation to the
next falls below the stopValue.
stopValue
Threshold value for terminating an analysis prior to
nStepesPRC.
maxAttempts
Maximum number attempts made
in while loop within the PRC algorithm.
If reached, the algorithm will terminate with an error message.
By default, this is infinite, and thus there is no
effective limit without user intervention.
diagnosticPRCmode
If TRUE
(not the default), the function will be very
noisy about characteristics of the PRC algorithm as it runs.
jobName
Optional job name.
saveData
Option to save various
run information during the analysis,
including summary statistics from analyses,
output to external .Rdata and .txt files.
verboseParticles
If TRUE (the default),
a large amount of information about parameter estimates
and acceptance of particles is output to
console via message as doRun_prc runs.
abcTolerance
Proportion of accepted simulations.
checkpointFile
Optional file name for checkpointing simulations.
checkpointFreq
Saving frequency for checkpointing.
savesims
Option to save individual simulations, output to a .Rdata file.
Details
Both doRun functions take an input phylogeny (phy),
observed trait data set (traits), models
(intrinsicFn, extrinsicFn), and priors
(startingPriorsValues, startingPriorsFns,
intrinsicPriorsValues,
intrinsicPriorsFns, extrinsicPriorsValues,
extrinsicPriorsFns). Pulling from the priors, it
simulates an initial set of simulations (nInitialSims).
This set of simulations is boxcox transformed , and a PLS regression (see
methodsPLS) is performed for each free parameter
to determine the most informative summary statistics (using
variance.cutoff). The euclidean distance is calculated
between each each initial simulation's most informative summary
statistics and the input observed data.
Following that step, the approach of the two functions diverge drastically.
For function doRun_rej, those simulations whose
most informative summary statistics fall below
abcTolerance are kept as accepted 'particles'
(simulations runs), describing the
posterior distribution of parameters. No additional
generations of simulations are performed.
Coversely, function doRun_prc performs an ABC-PRC analysis,
which follows a much more complicated algorithm with additional
generations. In PRC, tolerance is set based on
epsilonProportion and epsilonMultiplier, and the analysis
begins with generation 1 and continues through a
number of generations equal to nStepsPRC.
Single simulation runs ('particles') are accepted if the distance of the
most informative summary stats to the original data
is less than this tolerance. A generation
is complete when enough particles (numParticles) have been accepted. These
particles make up the distribution for the next generation. The accepted
particles from the final generation describe the posterior distributions of
parameters.
Value
The output of these two functions are lists,
composed of multiple objects,
which differ slightly in their content among the
two functions. For doRun_prc, the output is:
- input.data
Input variables:
jobName, nTaxa (number of taxa),
nInitialSims, generation.time, TreeYears,
timeStep, totalGenerations,
epsilonProportion, epsilonMultiplier,
nRuns (number of runs), nStepsPRC,
numParticles, standardDevFactor.
- priorList
List of prior distributions. This is used
for doing post-analysis comparisons between prior and
posterior distributions, such as with function plotPosteriors.
- particleDataFrame
DataFrame with information from each
simulation, including generation, attempt, id, parentid,
distance, weight, and parameter states.
- toleranceVector
Final tolerance vector.
- phy
Input phylogeny.
- traits
Input traits.
- simTime
Processor time for initial simulations.
- time.per.gen
Processor time for subsequent generations.
- postSummary
Summarizes the posterior distribution from
the final generation for all free parameters, giving the mean,
standard deviation and Highest Posterior
Density (at a 0.8 alpha) for each parameter.
If nRuns is greater than 1, the output from
doRun_prc will be a list object composed of
multiple output lists, as described.
For doRun_rej, the output is:
- input.data
Input variables:
jobName, nTaxa (number of taxa),
nInitialSims, generation.time, TreeYears,
timeStep, totalGenerations,
epsilonProportion, epsilonMultiplier,
nRuns (number of runs), nStepsPRC,
numParticles, standardDevFactor.
- priorList
List of prior distributions. This is
used for doing post-analysis comparisons between prior
and posterior distributions, such as
with function plotPosteriors.
- phy
Input phylogeny.
- traits
Input traits.
- trueFreeValuesANDSummaryValues
Parameter
estimates and summary stats from all sims.
- simTime
Processor time for simulations.
- abcDistancesRaw
Euclidean distances
for each simulation and free parameter.
- particleDataFrame
DataFrame with information from each
simulation, including generation, attempt, id,
parentid, distance, weight, and parameter states.
- postSummary
Summarizes the posterior distribution
from the final generation for all free parameters,
giving the mean, standard deviation and Highest Posterior
Density (at a 0.8 alpha) for each parameter.
- parMeansList
A list of parameter means for both
fixed and unfixed parameters, sorted into a list of
three vectors (starting, intrinsic, extrinsic).
Author(s)
Brian O'Meara and Barb Banbury
References
Sisson et al. 2007, Wegmann et
al. 2009
Examples
set.seed(1)
data(simRunExample)
# NOTE: the example analyses below sample too few particles,
# over too few steps, with too few starting simulations
# - all for the sake of examples that demonstrate these
# within a reasonable time-frame
## Please set these values to more realistic levels for your analyses!
resultsPRC <- doRun_prc(
phy = simPhyExample,
traits = simCharExample,
intrinsicFn = brownianIntrinsic,
extrinsicFn = nullExtrinsic,
startingPriorsFns = "normal",
startingPriorsValues = list(c(mean(simCharExample[, 1]),
sd(simCharExample[, 1]))),
intrinsicPriorsFns = c("exponential"),
intrinsicPriorsValues = list(10),
extrinsicPriorsFns = c("fixed"),
extrinsicPriorsValues = list(0),
generation.time = 10000,
nRuns = 2,
nStepsPRC = 3,
numParticles = 20,
nInitialSimsPerParam = 10,
jobName = "examplerun_prc",
stopRule = FALSE,
multicore = FALSE,
coreLimit = 1
)
resultsPRC
#one should make sure priors are uniform with doRun_rej!
resultsRej <- doRun_rej(
phy = simPhyExample,
traits = simCharExample,
intrinsicFn = brownianIntrinsic,
extrinsicFn = nullExtrinsic,
startingPriorsFns = "normal",
startingPriorsValues = list(c(mean(simCharExample[, 1]),
sd(simCharExample[, 1]))),
intrinsicPriorsFns = c("exponential"),
intrinsicPriorsValues = list(10),
extrinsicPriorsFns = c("fixed"),
extrinsicPriorsValues = list(0),
generation.time = 10000,
jobName = "examplerun_rej",
abcTolerance = 0.05,
multicore = FALSE,
coreLimit = 1
)
resultsRej
bomeara/treevo documentation built on Aug. 19, 2023, 6:52 p.m.
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| doRun | R Documentation |
Run Approximate Bayesian Computation for Phylogenetic Comparative Methods
Description
The doRun functions are the main interface for
TreEvo users to do Approximate Bayesian Computation (ABC) analysis,
effectively wrapping the simulateWithPriors functions
to perform simulations, which themselves are wrappers for
the doSimulation function. The two current doRun
functions are doRun_prc which applies a partial
rejection control (PRC) ABC analysis over multiple generations of simulations,
and doRun_rej which performs a full rejection ('rej') ABC analysis.
Usage
doRun_prc(
phy,
traits,
intrinsicFn,
extrinsicFn,
startingPriorsValues,
startingPriorsFns,
intrinsicPriorsValues,
intrinsicPriorsFns,
extrinsicPriorsValues,
extrinsicPriorsFns,
numParticles = 300,
nStepsPRC = 5,
nRuns = 2,
nInitialSims = NULL,
nInitialSimsPerParam = 100,
generation.time = 1000,
TreeYears = max(branching.times(phy)) * 1e+06,
multicore = FALSE,
coreLimit = NA,
multicoreSuppress = FALSE,
standardDevFactor = 0.2,
epsilonProportion = 0.7,
epsilonMultiplier = 0.7,
validation = "CV",
scale = TRUE,
variance.cutoff = 95,
stopRule = FALSE,
stopValue = 0.05,
maxAttempts = Inf,
diagnosticPRCmode = FALSE,
jobName = NA,
saveData = FALSE,
verboseParticles = TRUE
)
doRun_rej(
phy,
traits,
intrinsicFn,
extrinsicFn,
startingPriorsValues,
startingPriorsFns,
intrinsicPriorsValues,
intrinsicPriorsFns,
extrinsicPriorsValues,
extrinsicPriorsFns,
generation.time = 1000,
TreeYears = max(branching.times(phy)) * 1e+06,
multicore = FALSE,
coreLimit = NA,
validation = "CV",
scale = TRUE,
nInitialSims = NULL,
nInitialSimsPerParam = 100,
variance.cutoff = 95,
standardDevFactor = 0.2,
jobName = NA,
abcTolerance = 0.1,
checkpointFile = NULL,
checkpointFreq = 24,
savesims = FALSE
)
Arguments
phy |
A phylogenetic tree, in package |
traits |
Data matrix with rownames identical to |
intrinsicFn |
Name of (previously-defined) function that governs how traits evolve within a lineage, regardless of the trait values of other taxa. |
extrinsicFn |
Name of (previously-defined) function that governs how traits evolve within a lineage, based on their own ('internal') trait vlaue and the trait values of other taxa. |
startingPriorsValues |
A list of the same length as
the number of prior distributions specified in
|
startingPriorsFns |
Vector containing names of prior distributions to
use for root states: can be one of
|
intrinsicPriorsValues |
A list of the same length
as the number of prior distributions specified
in |
intrinsicPriorsFns |
Vector containing names of prior distributions to
use for intrinsic function parameters: can be one of
|
extrinsicPriorsValues |
A list of the same length as
the number of prior distributions specified in |
extrinsicPriorsFns |
Vector containing names of prior distributions to
use for extrinsic function parameters: can be one of
|
numParticles |
Number of accepted particles per PRC generation. |
nStepsPRC |
Number of PRC generations to run. |
nRuns |
Number of independent PRC runs to be performed,
each consisting of independent sets of
initial simulations and PRC generations. Note that runs are
run sequentially, and not in parallel,
as the generation of particles within each run
makes use of the multicore functionality.
If |
nInitialSims |
Number of initial simulations used to calibrate particle rejection control algorithm. If not given, this will be a function of the number of freely varying parameters. |
nInitialSimsPerParam |
If |
generation.time |
The number of years per generation.
This sets the coarseness of the simulation; if it's set to 1000,
for example, the population's trait values change every 1000
calendar years. Note that this is in calendar years (see description
for argument |
TreeYears |
The amount of calendar time from the root
to the furthest tip. Most trees in macroevolutionary studies are dated with
branch lengths in units of millions of years, and thus the
default for this argument is |
multicore |
Whether to use multicore, default is |
coreLimit |
Maximum number of cores to be used. |
multicoreSuppress |
This argument suppresses the
multicore code and will apply a plain vanilla serial |
standardDevFactor |
Standard deviation for mutating states each time a new particle is generated in a PRC generation. |
epsilonProportion |
Sets tolerance for initial simulations. |
epsilonMultiplier |
Sets tolerance on subsequent PRC generations. |
validation |
Character argument controlling what
validation procedure is used by |
scale |
This argument is passed to |
variance.cutoff |
Minimum threshold percentage of variance explained for the number of components included in the final PLS model fit. This value is a percentage and must be between 0 and 100. Default is 95 percent. |
stopRule |
If |
stopValue |
Threshold value for terminating an analysis prior to
|
maxAttempts |
Maximum number attempts made
in |
diagnosticPRCmode |
If |
jobName |
Optional job name. |
saveData |
Option to save various run information during the analysis, including summary statistics from analyses, output to external .Rdata and .txt files. |
verboseParticles |
If |
abcTolerance |
Proportion of accepted simulations. |
checkpointFile |
Optional file name for checkpointing simulations. |
checkpointFreq |
Saving frequency for checkpointing. |
savesims |
Option to save individual simulations, output to a .Rdata file. |
Details
Both doRun functions take an input phylogeny (phy),
observed trait data set (traits), models
(intrinsicFn, extrinsicFn), and priors
(startingPriorsValues, startingPriorsFns,
intrinsicPriorsValues,
intrinsicPriorsFns, extrinsicPriorsValues,
extrinsicPriorsFns). Pulling from the priors, it
simulates an initial set of simulations (nInitialSims).
This set of simulations is boxcox transformed , and a PLS regression (see
methodsPLS) is performed for each free parameter
to determine the most informative summary statistics (using
variance.cutoff). The euclidean distance is calculated
between each each initial simulation's most informative summary
statistics and the input observed data.
Following that step, the approach of the two functions diverge drastically.
For function doRun_rej, those simulations whose
most informative summary statistics fall below
abcTolerance are kept as accepted 'particles'
(simulations runs), describing the
posterior distribution of parameters. No additional
generations of simulations are performed.
Coversely, function doRun_prc performs an ABC-PRC analysis,
which follows a much more complicated algorithm with additional
generations. In PRC, tolerance is set based on
epsilonProportion and epsilonMultiplier, and the analysis
begins with generation 1 and continues through a
number of generations equal to nStepsPRC.
Single simulation runs ('particles') are accepted if the distance of the
most informative summary stats to the original data
is less than this tolerance. A generation
is complete when enough particles (numParticles) have been accepted. These
particles make up the distribution for the next generation. The accepted
particles from the final generation describe the posterior distributions of
parameters.
Value
The output of these two functions are lists,
composed of multiple objects,
which differ slightly in their content among the
two functions. For doRun_prc, the output is:
- input.data
Input variables:
jobName,nTaxa(number of taxa),nInitialSims,generation.time,TreeYears,timeStep,totalGenerations,epsilonProportion,epsilonMultiplier,nRuns(number of runs),nStepsPRC,numParticles,standardDevFactor.- priorList
List of prior distributions. This is used for doing post-analysis comparisons between prior and posterior distributions, such as with function
plotPosteriors.- particleDataFrame
DataFrame with information from each simulation, including generation, attempt,
id,parentid, distance, weight, and parameter states.- toleranceVector
Final tolerance vector.
- phy
Input phylogeny.
- traits
Input traits.
- simTime
Processor time for initial simulations.
- time.per.gen
Processor time for subsequent generations.
- postSummary
Summarizes the posterior distribution from the final generation for all free parameters, giving the mean, standard deviation and Highest Posterior Density (at a 0.8 alpha) for each parameter.
If nRuns is greater than 1, the output from
doRun_prc will be a list object composed of
multiple output lists, as described.
For doRun_rej, the output is:
- input.data
Input variables:
jobName,nTaxa(number of taxa),nInitialSims,generation.time,TreeYears,timeStep,totalGenerations,epsilonProportion,epsilonMultiplier,nRuns(number of runs),nStepsPRC,numParticles,standardDevFactor.- priorList
List of prior distributions. This is used for doing post-analysis comparisons between prior and posterior distributions, such as with function
plotPosteriors.- phy
Input phylogeny.
- traits
Input traits.
- trueFreeValuesANDSummaryValues
Parameter estimates and summary stats from all sims.
- simTime
Processor time for simulations.
- abcDistancesRaw
Euclidean distances for each simulation and free parameter.
- particleDataFrame
DataFrame with information from each simulation, including generation, attempt, id, parentid, distance, weight, and parameter states.
- postSummary
Summarizes the posterior distribution from the final generation for all free parameters, giving the mean, standard deviation and Highest Posterior Density (at a 0.8 alpha) for each parameter.
- parMeansList
A list of parameter means for both fixed and unfixed parameters, sorted into a list of three vectors (starting, intrinsic, extrinsic).
Author(s)
Brian O'Meara and Barb Banbury
References
Sisson et al. 2007, Wegmann et al. 2009
Examples
set.seed(1)
data(simRunExample)
# NOTE: the example analyses below sample too few particles,
# over too few steps, with too few starting simulations
# - all for the sake of examples that demonstrate these
# within a reasonable time-frame
## Please set these values to more realistic levels for your analyses!
resultsPRC <- doRun_prc(
phy = simPhyExample,
traits = simCharExample,
intrinsicFn = brownianIntrinsic,
extrinsicFn = nullExtrinsic,
startingPriorsFns = "normal",
startingPriorsValues = list(c(mean(simCharExample[, 1]),
sd(simCharExample[, 1]))),
intrinsicPriorsFns = c("exponential"),
intrinsicPriorsValues = list(10),
extrinsicPriorsFns = c("fixed"),
extrinsicPriorsValues = list(0),
generation.time = 10000,
nRuns = 2,
nStepsPRC = 3,
numParticles = 20,
nInitialSimsPerParam = 10,
jobName = "examplerun_prc",
stopRule = FALSE,
multicore = FALSE,
coreLimit = 1
)
resultsPRC
#one should make sure priors are uniform with doRun_rej!
resultsRej <- doRun_rej(
phy = simPhyExample,
traits = simCharExample,
intrinsicFn = brownianIntrinsic,
extrinsicFn = nullExtrinsic,
startingPriorsFns = "normal",
startingPriorsValues = list(c(mean(simCharExample[, 1]),
sd(simCharExample[, 1]))),
intrinsicPriorsFns = c("exponential"),
intrinsicPriorsValues = list(10),
extrinsicPriorsFns = c("fixed"),
extrinsicPriorsValues = list(0),
generation.time = 10000,
jobName = "examplerun_rej",
abcTolerance = 0.05,
multicore = FALSE,
coreLimit = 1
)
resultsRej
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