ouxy (version 2.1)

oubmbmmodel: Simulate traits under OUBMBM model given a set of parameters

Description

Simulate traits under OUBMBM model given a set of model parameters, regression parameters, tree and ancestral values.

Usage

oubmbmmodel(model.params, reg.params, root = root, tree = tree)

Arguments

model.params

A vector of model parameters including alpha.y: force parameter of response trait, sigmasq.x: rate parameter of covariate and tau: rate parameter of response trait

reg.params

A vector of regression paramters including b0: intercept parameter of regression, b1: slope parameter of first covariate, b2: slope paramter of the second covariate

root

A vector of numerical values for root of species estimated from OUprior estimated from OUprior

tree

An ape: tree object stored in phylo format

Value

Returns the trait vectors \(Y=(y_1,y_2,\cdots,y_n)', X_1=(x_{1,1},x_{1.2},\cdots,x_{1,n})',X_2=(x_{2,1},x_{2,2},\cdots,x_{2,n})'\) simulated from the model.

Details

The model requires user to input model parameters \(\alpha_y, \sigma^2_x,\tau\) and regression parameters \(b_0,b_1,b_2\), a tree object, trait dataset (one response, two covariate), ancestral values(root) which is estimated by BM or OU model from geiger. Then the algorithm starts from the root and apply the tree traversal algorithm to simulate trait on each node according the OUOUBM dynamics.

References

1. Jhwueng, D-C. (2019) Statistical modeling for adaptive trait evolution. Under review.

2. Jhwueng, D-C., and Vasileios Maroulas. "Adaptive trait evolution in random environment." Journal of Applied Statistics 43.12 (2016): 2310-2324.

3. Hansen, Thomas F., Jason Pienaar, and Steven Hecht Orzack. "A comparative method for studying adaptation to a randomly evolving environment." Evolution: International Journal of Organic Evolution 62.8 (2008): 1965-1977.

Examples

# NOT RUN {
library(ape)
tree<-rcoal(5)
tree<-reorder(tree,"postorder")
root<-list(y.ou.sigmsq=1 ,y.ou.root=0, x1.ou.root=0, x2.ou.root=0,x1.bm.root=0, x2.bm.root=0)
model.params<-c(0.5,1,0.2)
names(model.params)<-c("alpha.y","sigmasq.x","tau")
reg.params<-c(0,1,1)
names(reg.params)<-c("b0","b1","b2")
oubmbmmodel(model.params,reg.params,root=root,tree=tree)

# }