PCMParam

Global and S3 generic functions for manipulating model parameters.
The parameters in a PCM are named objects with a class attribute specifying
the main type and optional properties (tags).
S3 generic functions:<dl class="dl-horizontal">
<dt>PCMParamCount()</dt><dd>Counting the number of actual numeric parameters (used,
 e.g. for calculating information scores, e.g. AIC);</dd>
<dt>PCMParamLoadOrStore(), PCMParamLoadOrStore()</dt><dd>Storing/loading a parameter
to/from a numerical vector;</dd>
<dt>PCMParamLowerLimit(),PCMParamUpperLimit()</dt><dd>Specifying parameter upper and lower limits;</dd>
<dt>PCMParamRandomVecParams()</dt><dd>Generating a random parameter vector;</dd>
</dl>For all the above properties, check-functions are defined, e.g. `is.Local(o)`,
`is.Global(o)`, `is.ScalarParameter(o)`, `is.VectorParameter`, etc.

Phylogenetic comparative methods represent models of continuous trait
data associated with the tips of a phylogenetic tree. Examples of such models
are Gaussian continuous time branching stochastic processes such as Brownian
motion (BM) and Ornstein-Uhlenbeck (OU) processes, which regard the data at the
tips of the tree as an observed (final) state of a Markov process starting from
an initial state at the root and evolving along the branches of the tree. The
PCMBase R package provides a general framework for manipulating such models.
This framework consists of an application programming interface for specifying
data and model parameters, and efficient algorithms for simulating trait evolution
under a model and calculating the likelihood of model parameters for an assumed
model and trait data. The package implements a growing collection of models,
which currently includes BM, OU, BM/OU with jumps, two-speed OU as well as mixed
Gaussian models, in which different types of the above models can be associated
with different branches of the tree. The PCMBase package is limited to
trait-simulation and likelihood calculation of (mixed) Gaussian phylogenetic
models. The PCMFit package provides functionality for ML and Bayesian fit of
these models to tree and trait data. The package web-site
<https://venelin.github.io/PCMBase/>
provides access to the documentation and other resources.

PCMParam: Module PCMParam

Description

Arguments