The phylogeny in ouch format. All of the internal nodes have to be
uniquely named. The tree can be obtained from e.g. a nexus file by the
read.nexus function
from the ape package and converted into the ouch format by ouch's
ape2ouch function. See the example of how to correct the internal nodes.
X0
The ancestral, root state.
Sigma
The diffusion matrix of the Brownian motion.
dropInternal
Logical whether the simulated values at the internal nodes be
changed to NA or not.
M.error
An optional measurement error covariance matrix. The program tries to recognizes
the structure of matrix passed and accepts the following possibilities :
a single number that will be on the diagonal of the covariance matrix
a m element vector with each value corresponding to a variable and the
covariance matrix will have that vector repeated on its diagonal,
a nxm element vector a diagonal matrix with this vector on the diagonal,
a m x m ((number of variables) x (number of variables)) matrix it is assumed
that the measurement errors are independent between observations so the resulting
covariance structure is block diagonal,
a list of length m (number of variables), each list element is the
covariance structure for the appropriate variable, either a single number
(each observations has same variance), vector (of length n for each observation),
or full matrix,
matrix of size mn x mn (m - number of variables, n - number of observations)
the measurement error covaraince provided as is,
NULL no measurement error
fullTrajectory
Should the full realization of the process or only node and tip values
be returned
jumpsetup
Either NULL or list describing the jump at speciation. In the second
case:
jumptypeIn what way does the jump take place. Possible values are
"ForBoth" the jump occurs at speciation and is common to both daughter lineages,
"RandomLineage" the jump occurs just after speciation affecting exactly one
daughter lineage, both desceding branches have the same chance of being affected,
"JumpWithProb" the jump occurs with probability jumpprob
just after speciation independently on each daughter lineage independently.
jumpprobA value in [0,1] indictating the probability of a
jump taking place, only matters if jumptype is "JumpWithProb"
or "JumpWithProb".
jumpdistribThe distribution of the jump, currently only can take
value "Normal".
vMeanThe expected value of the jump, a vector of appropriate
length if the trait is multivariate.
mCovThe variance of the jump, a matrix of appropriate
dimensions if the trait is multivariate.
Value
If fullTrajectory is FALSE then
returns a data.frame with each row corresponding to a tree node and each column to a trait.
Otherwise returns a more complex object describing the full realization of the process on the tree.
If dropInternal is TRUE
then the entries for the internal nodes are changed to NAs.
Author(s)
Krzysztof Bartoszek
References
Bartoszek, K. (2014)
Quantifying the effects of anagenetic and cladogenetic evolution.
Mathematical Biosciences 254:42-57.
Bartoszek, K. and Pienaar, J. and Mostad. P. and Andersson, S. and Hansen, T. F. (2012)
A phylogenetic comparative method for studying multivariate adaptation.
Journal of Theoretical Biology 314:204-215.
Butler, M.A. and A.A. King (2004)
Phylogenetic comparative analysis: a modeling approach for adaptive evolution.
American Naturalist 164:683-695.
Felsenstein, J. (1985)
Phylogenies and the comparative method.
American Naturalist 125:1-15.
Hansen, T.F. and Bartoszek, K. (2012)
Interpreting the evolutionary regression: the interplay between observational and biological
errors in phylogenetic comparative studies.
Systematic Biology 61(3):413-425.
Pienaar et al (in prep) An overview of comparative methods for
testing adaptation to external environments.
See Also
BrownianMotionModel, SummarizeBM
Examples
## Not run: ##It takes too long to run this
### We will first simulate a small phylogenetic tree using functions from ape and ouch.
### For simulating the tree one could also use alternative functions, eg. sim.bd.taxa
### from the TreeSim package
phyltree<-ape2ouch(rtree(5))
### Correct the names of the internal node labels.
phyltree@nodelabels[1:(phyltree@nnodes-phyltree@nterm)]<-as.character(
1:(phyltree@nnodes-phyltree@nterm))
### Define Brownian motion parameters to be able to simulate data
### under the Brownian motion model.
BMparameters<-list(vX0=matrix(0,nrow=3,ncol=1),
Sxx=rbind(c(1,0,0),c(0.2,1,0),c(0.3,0.25,1)))
### Now simulate the data and remove the values corresponding to the internal nodes.
jumpobj<-list(jumptype="RandomLineage",jumpprob=0.5,jumpdistrib="Normal",
vMean=rep(0,3),mCov=diag(1,3,3))
BMdata<-simulBMProcPhylTree(phyltree,X0=BMparameters$vX0,Sigma=BMparameters$Sxx,jumpsetup=jumpobj)
## End(Not run)
providing 
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