SummarizeOUCH  R Documentation 
Compiles a summary (appropriate moments, conditional moments, information criteria) of parameters of a (multivariate) OU model at a given time point. The user is recommended to install the suggested package PCMBaseCpp which significantly speeds up the calculations (see Details).
SummarizeOUCH(phyltree, mData, modelParams, regimes = NULL,
regimes.times = NULL, t = c(1), dof = NULL, M.error = NULL,
predictors = NULL, Atype = "Invertible", Syytype = "UpperTri", min_bl = 0.0003)
phyltree 
The phylogeny in 
mData 
A matrix with the rows corresponding to the tip species while the columns correspond to the traits.
The rows should be named by species 
modelParams 
A list of model parameters, as returned in 
regimes 
A vector or list of regimes. If vector then each entry corresponds to each of 
regimes.times 
A list of vectors for each tree node, it starts with 0 and ends with the current time of the species.
In between are the times where the regimes (niches) changed. If 
t 
A vector of time points at which the summary is to be calculated. This allows for one to study (and plot) the (conditional) mean and covariance as functions of time. The function additionally returns the parameter summary at the tree's height. 
dof 
Number of unknown parameters in the model, can be extracted from the output of

M.error 
An optional measurement error covariance structure. The measurement errors between species are assumed independent. The program tries to recognize the structure of the passed matrix and accepts the following possibilities :
From version 
predictors 
A vector giving the numbers of the columns from 
Atype 
What class does the A matrix in the multivariate OUBM model belong to, possible values :

Syytype 
What class does the Syy matrix in the multivariate OUBM model belong to, possible values :

min_bl 
Value to which PCMBase's 
The likelihood calculations are done by the PCMBase package. However, there is a C++ backend, PCMBaseCpp. If it is not available, then the likelihood is calculated slower using pure R. However, with the calculations in C++ up to a 100fold increase in speed is possible (more realistically 1020 times). The PCMBaseCpp package is available from https://github.com/venelin/PCMBaseCpp.
The setting Atype="Any"
means that one assumes the matrix A
is eigendecomposable.
If A
is defective, then the output will be erroneous.
From version 2.0.0
of mvSLOUCH the data has to be passed as a matrix.
To underline this the data parameter's name has been changed to mData
.
From version 2.0.0
of mvSLOUCH the parameter calcCI
has been removed.
The package now offers the possibility of bootstrap confidence intervals, see
function parametric.bootstrap
.
A list for each provided time point. See the help of mvslouchModel
for what the
summary at each time point is.
Krzysztof Bartoszek
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 314:204215.
Butler, M.A. and A.A. King (2004) Phylogenetic comparative analysis: a modeling approach for adaptive evolution. American Naturalist 164:683695.
Hansen, T.F. (1997) Stabilizing selection and the comparative analysis of adaptation. Evolution 51:13411351.
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):413425.
Pienaar et al (in prep) An overview of comparative methods for testing adaptation to external environments.
hansen
, ouchModel
, simulOUCHProcPhylTree
, parametric.bootstrap
RNGversion(min(as.character(getRversion()),"3.6.1"))
set.seed(12345, kind = "MersenneTwister", normal.kind = "Inversion")
### We will first simulate a small phylogenetic tree using functions from ape.
### For simulating the tree one could also use alternative functions, e.g. sim.bd.taxa
### from the TreeSim package
phyltree<ape::rtree(5)
## The line below is not necessary but advisable for speed
phyltree<phyltree_paths(phyltree)
### Define a vector of regimes.
regimes<c("small","small","large","small","small","large","large","large")
### Define the SDE parameters to be able to simulate data under the OUOU model.
OUOUparameters<list(vY0=matrix(c(1,1,0.5),nrow=3,ncol=1),
A=rbind(c(9,0,0),c(0,5,0),c(0,0,1)),mPsi=cbind("small"=c(1,1,0.5),
"large"=c(1,1,0.5)),Syy=rbind(c(1,0.25,0.3),c(0,1,0.2),c(0,0,1)))
### Now simulate the data.
OUOUdata<simulOUCHProcPhylTree(phyltree,OUOUparameters,regimes,NULL)
OUOUdata<OUOUdata[phyltree$tip.label,,drop=FALSE]
## Here we do not do any recovery step
OUOU.summary<SummarizeOUCH(phyltree,OUOUdata,OUOUparameters,
regimes,t=c(1),dof=15)
RNGversion(as.character(getRversion()))
## Not run: ##It takes too long to run this
## Now we take a less trivial simulation setup
### Recover the parameters of the OUOU model.
OUOUestim<ouchModel(phyltree,OUOUdata,regimes,Atype="DecomposablePositive",
Syytype="UpperTri",diagA="Positive",maxiter=c(10,100))
### Summarize them.
OUOU.summary<SummarizeOUCH(phyltree,OUOUdata,OUOUestim$FinalFound$ParamsInModel,
regimes,t=c(1),dof=OUOUestim$FinalFound$ParamSummary$dof)
### And finally bootstrap with particular interest in the evolutionary regression
OUOUbootstrap<parametric.bootstrap(estimated.model=OUOUestim,phyltree=phyltree,
values.to.bootstrap=c("evolutionary.regression"),regimes=regimes,root.regime="small",
M.error=NULL,predictors=c(3),kY=NULL,numboot=5,Atype=NULL,Syytype=NULL,diagA=NULL)
## End(Not run)
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