PCM | R Documentation |
This is the entry-point function for creating model objects
within the PCMBase framework representing a single model-type with one or
several model-regimes of this type associated with the branches of a tree.
For mixed Gaussian phylogenetic models, which enable multiple model-types,
use the MixedGaussian
function.
PCM( model, modelTypes = class(model)[1], k = 1L, regimes = 1L, params = NULL, vecParams = NULL, offset = 0L, spec = NULL, ... )
model |
This argument can take one of the following forms:
The Details section explains how these two types of input are processed. |
modelTypes |
a character string vector specifying a set (family) of model-classes, to which the constructed model object belongs. These are used for model-selection. |
k |
integer denoting the number of traits (defaults to 1). |
regimes |
a character or integer vector denoting the regimes. |
params |
NULL (default) or a list of parameter values (scalars, vectors, matrices, or arrays) or sub-models (S3 objects inheriting from the PCM class). See details. |
vecParams |
NULL (default) or a numeric vector the vector representation of the variable parameters in the model. See details. |
offset |
integer offset in vecParams; see Details. |
spec |
NULL or a list specifying the model parameters (see
|
... |
additional parameters intended for use by sub-classes of the PCM class. |
This is an S3 generic. The PCMBase package defines three methods for it:
PCM.PCM: A default constructor for any object with a class inheriting from "PCM".
PCM.character: A default PCM constructor from a character string specifying the type of model.
PCM.default: A default constructor called when no other constructor is found. When called this constructor raises an error message.
an object of S3 class as defined by the argument model
.
MixedGaussian
# a Brownian motion model with one regime modelBM <- PCM(model = "BM", k = 2) # print the model modelBM # a BM model with two regimes modelBM.ab <- PCM("BM", k = 2, regimes = c("a", "b")) modelBM.ab # print a single parameter of the model (in this case, the root value) modelBM.ab$X0 # assign a value to this parameter (note that the brackets [] are necessary # to preserve the parameter attributes): modelBM.ab$X0[] <- c(5, 2) PCMNumTraits(modelBM) PCMNumRegimes(modelBM) PCMNumRegimes(modelBM.ab) # number of numerical parameters in the model PCMParamCount(modelBM) # Get a vector representation of all parameters in the model PCMParamGetShortVector(modelBM) # Limits for the model parameters: lowerLimit <- PCMParamLowerLimit(modelBM) upperLimit <- PCMParamUpperLimit(modelBM) # assign the model parameters at random: this will use uniform distribution # with boundaries specified by PCMParamLowerLimit and PCMParamUpperLimit # We do this in two steps: # 1. First we generate a random vector. Note the length of the vector equals PCMParamCount(modelBM) randomParams <- PCMParamRandomVecParams(modelBM, PCMNumTraits(modelBM), PCMNumRegimes(modelBM)) randomParams # 2. Then we load this random vector into the model. PCMParamLoadOrStore(modelBM, randomParams, 0, PCMNumTraits(modelBM), PCMNumRegimes(modelBM), TRUE) print(modelBM) PCMParamGetShortVector(modelBM) # generate a random phylogenetic tree of 10 tips tree <- ape::rtree(10) #simulate the model on the tree traitValues <- PCMSim(tree, modelBM, X0 = modelBM$X0) # calculate the likelihood for the model parameters, given the tree and the trait values PCMLik(traitValues, tree, modelBM) # create a likelihood function for faster processing for this specific model. # This function is convenient for calling in optim because it recieves and parameter # vector instead of a model object. likFun <- PCMCreateLikelihood(traitValues, tree, modelBM) likFun(randomParams)
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