View source: R/simulVasicekprocphyl.R
drawPhylProcess  R Documentation 
The function takes the output of the simulation functions and based on it plots the realization of the process on the tree. Can handle multiple traits, in this case each trait is plotted separately. The function does draw anything else (like axes) but the realization of the process. Any additions are up to the user.
drawPhylProcess(PhylTraitProcess, vTraitsToPlot=NULL, vColours = "black",
plotlayout = c(1, 1), additionalfigs = FALSE, modelParams = NULL,
EvolModel = NULL, xlimits = NULL, ylimits = NULL)
PhylTraitProcess 
The simulated realization of the process, the direct output of one of the package's simulation
function or a matrix (if 
vTraitsToPlot 
A vector providing the column numbers of the traits to plot. If 
vColours 
A vector of colours to be used for each trait. If length is less than the number of traits then colours are recycled 
plotlayout 
How many plots per page if more than one trait, i.e. 
additionalfigs 
Should additional items be plotted on each figure, the ancestral state and
deterministic,

modelParams 
List of model parameters. 
EvolModel 
The evolutionary model. 
xlimits 
The x limits of the plot. Can be useful to fix if one wants to have a number of graphs on the same scale. This can be either a vector of length 2 (minimum and maximum value of the xaxis), or a list of length equalling the number of traits with each entry being a vector of length 2 or a matrix with two columns and rows equalling the number of traits. If not provided then the value is just the minimum and maximum from the data. 
ylimits 
The y limits of the plot. Can be useful to fix if one wants to have a number of graphs on the same scale. This can be either a vector of length 2 (minimum and maximum value of the xaxis), or a list of length equalling the number of traits with each entry being a vector of length 2 or a matrix with two columns and rows equalling the number of traits. If not provided then the value is just the minimum and maximum from the data. 
Returns a meaningless NA value.
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.
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(3)
## 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")
#regimes<c("small","small","large","small","small","large")
regimes<c("small","small","large","small")
### Define SDE parameters to be able to simulate data under the OUOU model.
## 3D 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)))
## 2D model for speed on CRAN
OUOUparameters<list(vY0=matrix(c(1,1),nrow=2,ncol=1),
A=rbind(c(9,0),c(0,5)),mPsi=cbind("small"=c(1,1),
"large"=c(1,1)),Syy=rbind(c(1,0.25),c(0,1)))
### Now simulate the data keeping the whole trajectory
OUOUdata<simulOUCHProcPhylTree(phyltree,OUOUparameters,regimes,NULL,fullTrajectory=TRUE)
drawPhylProcess(PhylTraitProcess=OUOUdata,plotlayout=c(1,3))
RNGversion(as.character(getRversion()))
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