Description Usage Arguments Details Value
Perform simulated annealing to identify macroevolutionary cohorts.
1  find.cohorts(x, phy, kmax, control = list())

x 
A vector or matrix of tip statistics. If 
phy 
A phylogeny that inherits from class 
kmax 
The maximum number of cohorts to fit. Must be greater than 0. 
control 
An optional list containing parameters for the simulating annealing algorithm. Options are:

The function attempts to identify groups of tips (cohorts) with similar properties
based on a set of statistics computed for each tip. Typically these statistics will
have some relationship to an evolutionary rate, although any statistic will work in
principle. Every interal node in a phylogeny can define a cohort that may include all
or some of the tips descended from it (cohorts can be nested, so only a subset of tips
may be included). The function performs simulated annealing to determine the arrangement
of the k
cohorts that minimizes the total within cohort sum of squares of the
tip statistic(s) x
from k = 1
to k = kmax
. Cohorts are defined
by a set of k1
nonroot internal nodes plus the root node. The annealing chaing
stops once the temperature falls below the hard coded minimum of 0.0001. The number of
steps in the chain is therefore: ceiling((log(0.0001)log(T0))/log(alpha)) * iter
Note that the function does not determine the optimal number of cohorts, just the optimal
arrangement (based on minimizing the total within cohort sum of squares) given a fixed number.
However, x
may represent a distribution of tip statistics derived from a null model,
and this provides a means of determining the optimal number using other methods such as the
gap statistic.
If savecohorts=TRUE
a list with two components:
cohorts
An array of tip cohort memberships at each k
.
score
The total within group sum of squares implied by
the cohort memberships at each k
.
Otherwise a vector or matrix of the total within group sum of squares implied by
the cohort memberships at each k
.
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