fitMk

plot.fitMk

plot.gfit

tree

a vector of tip values for species; <code>names(x)</code> should be the species names. In the case of <code>plot.fitMk</code>, an object of class <code>"fitMk"</code>.

model. See <code>make.simmap</code> or <code>ace</code> for details.

model

fixed value of transition matrix <code>Q</code>, if one is desired.

fixedQ

optional arguments, including <code>pi</code>, the prior distribution at the root node (defaults to <code>pi="equal"</code>). For <code>plot</code> method optional arguments include (but may not be limited to): <code>signif</code>, the number of digits for the rates to be plotted; <code>main</code>, a character vector of length two with the headings for each subplot; <code>cex.main</code>, <code>cex.traits</code>, and <code>cex.rates</code>, font sizes for the various text elements of the plot; and <code>show.zeros</code>, a logical argument specifying whether or not to plot arrows with the ML estimated transition rate is not different from zero (with tolerance specified by the optional argument <code>tol</code>).


	This function fits a so-called M<em>k</em> model for discrete character evolution. It is primarily designed to be used inside of <code>make.simmap</code>.
	
	Two <code>plot</code> methods are available. <code>plot.fitMk</code> plots an object of class <code>"fitMk"</code> returned by <code>fitMk</code>. <code>plot.gfit</code> plots an object of class <code>"gfit"</code> from geiger's <code>fitDiscrete</code> function. Both plots portray the fitted model using a graph of arrows connecting states.


phylogenetics

maximum likelihood

comparative method

Package contains various functions for phylogenetic analysis.
This functionality is concentrated in the phylogenetic analysis of
comparative data from species. For example, the package includes
functions for Bayesian and ML ancestral state estimation; visual
simulation of trait evolution; fitting models of trait evolution
with multiple Brownian rates and correlations; visualizing
discrete and continuous character evolution using colors or
projections into trait space; identifying the location of a change
in the rate of character evolution on the tree; fast Brownian motion
simulation and simulation under several other models of
continuous trait evolution; fitting a model of correlated binary
trait evolution; locating the position of a fossil or an recently
extinct lineage on a tree using continuous character data with ML;
plotting lineage accumulation through time, including across
multiple trees (such as a Bayesian posterior sample); conducting
an analysis called stochastic character mapping, in which character
histories for a discrete trait are sampled from their posterior
probability distribution under a model; conducting a multiple
(i.e., partial) Mantel test; fitting a phylogenetic regression model
with error in predictor and response variables; conducting a
phylogenetic principal components analysis, a phylogenetic
regression, a reduced major axis regression, a phylogenetic
canonical correlation analysis, and a phylogenetic ANOVA; projecting
a tree onto a geographic map; simulating discrete character
histories on the tree; fitting a model in which a discrete character
evolves under the threshold model; visualization of cospeciation; a
simple statistical test for cospeciation between two trees. In
addition to this phylogenetic comparative method functionality, the
package also contains functions for a wide range of other purposes in
phylogenetic biology. For instance, functionality in this package
includes (but is not restricted to): adding taxa to a tree
(including randomly, everywhere, or automatically to genera);
generating all bi- and multi-furcating trees for a set of taxa;
reducing a phylogeny to its backbone tree; dropping tips or adding
tips to special types of phylogenetic trees; exporting a tree as an
XML file; converting a tree with a mapped character to a tree with
singleton nodes and one character state per edge; estimating a
phylogeny using the least squares method; simulating birth-death
trees under a range of conditions; rerooting trees; computing a
consensus tree under multiple methods, including via minimization
of the distance to other trees in the set; a wide range of
visualizations of trees; and a variety of other manipulations and
analyses that phylogenetic biologists may find useful for their
research.

fitMk: Fits Mk model

Description

Usage

Arguments

Value

References

See Also