rglinv

rglinv.glinv

rglinv.glinv_gauss

Simulate random trait values from the Gaussian branching process specified by <code>mod</code>.

A framework for analytically computing the asymptotic confidence intervals and maximum-likelihood estimates of a class of continuous-time Gaussian branching processes defined by Mitov V, Bartoszek K, Asimomitis G, Stadler T (2019) <doi:10.1016/j.tpb.2019.11.005>. The class of model includes the widely used Ornstein-Uhlenbeck and Brownian motion branching processes. The framework is designed to be flexible enough so that the users can easily specify their own sub-models, or re-parameterizations, and obtain the maximum-likelihood estimates and confidence intervals of their own custom models.

Hao Chi Kiang

glinvci

Phylogenetic Comparative Methods with Uncertainty Estimates

Woodrow Kiang

rglinv function

<dl><dt>mod</dt>
<dd>Either a <code>glinv_gauss</code> or <code>glinv</code> object.</dd>
<dt>par</dt>
<dd>Parameters underlying the simulation, in the same format as <code>lik.glinv_gauss</code> or <code>lik.glinv</code>.</dd>
<dt>Nsamp</dt>
<dd>Number of sample point to simulate.</dd>
<dt>simplify</dt>
<dd>If TRUE, <code>rglinv.glinv</code> returns an Nsamp-element list with each element being a tip-trait matrix;
otherwise, <code>rglinv.glinv</code> returns an Nsamp-element list with each element being an \(n\)-element list
of \(k\)-element trait vectors, where \(n\) is the number of tips and \(k\) is the dimension of
each trait vector.</dd></dl>

Arguments

Simulate random trait values from models. — rglinv

<dl>

<dt>mod</dt>
<dd>Either a <code>glinv_gauss</code> or <code>glinv</code> object.</dd>


<dt>par</dt>
<dd>Parameters underlying the simulation, in the same format as <code>lik.glinv_gauss</code> or <code>lik.glinv</code>.</dd>


<dt>Nsamp</dt>
<dd>Number of sample point to simulate.</dd>


<dt>simplify</dt>
<dd>If TRUE, <code>rglinv.glinv</code> returns an Nsamp-element list with each element being a tip-trait matrix;
otherwise, <code>rglinv.glinv</code> returns an Nsamp-element list with each element being an \(n\)-element list
of \(k\)-element trait vectors, where \(n\) is the number of tips and \(k\) is the dimension of
each trait vector.</dd>

</dl>

rglinv: Simulate random trait values from models.

Description

Usage

Value

Arguments