mvMORPH (version 1.0.2)

mvEB: Multivariate Early Burst model of continuous traits evolution

Description

This function allows the fitting of multivariate Early Burst (EB) or ACDC model of evolution.

Usage

mvEB(tree, data, error = NULL, low = -3, up = 0, tol=c(0.00000001,Inf),
 scale.height = FALSE, control=list(maxit=20000), pseudoinverse=FALSE,
 diagnostic = TRUE, echo = TRUE)

Arguments

tree

Phylogenetic tree (phylo object).

data

Matrix or data frame with species in rows and continuous traits in columns (preferentially with names and in the same order than in the tree).

error

Matrix or data frame with species in rows and continuous traits standard error (squared) in columns.

low

Lower bound of the exponent of the relationship between rate and time in the exponential change model.

up

Upper (maximum) bound of the exponent of the relationship between rate and time in the exponential change model. Default value is zero for a decelerating model (see details).

tol

Optimizer bounds for the search of the evolutionary rate matrix.

scale.height

Scale the tree to relative length.

control

Max. bound for the number of iteration of the optimizer; other options can be fixed on the list (see ?optim or ?subplex).

pseudoinverse

Whether Moore-Penrose pseudoinverse should be used in calculation (slower).

diagnostic

Whether the diagnostic on convergence and reliability of the estimates must be returned.

echo

Whether the summary statistics must be returned (or just stored).

Value

• LogLik
  The log-likelihood of the optimal model.
• AIC
  Akaike Information Criterion for the optimal model.
• AICc
  Sample size-corrected AIC.
• r
  Exponent rate.
• rates.m
  Evolutionary character rates matrix.
• anc
  Estimated ancestral states.
• convergence
  Convergence status of the optimizing function; "0" mean convergence (convergence.cons for the constrained model) (See ?optim for details).
• hess.values
  Reliability of the likelihood estimates calculated through the eigen-decomposition of the hessian matrix. "0" mean that a reliable estimate has been reached (hess.values.cons for the constrained model). See ?mvOU for details.

Details

The Early Burst model (Harmon et al. 2010) is a special case of the ACDC model of Blomberg et al. (2003). Using an upper bound larger than zero transform the model to the accelerating rates of character evolution of Blomberg et al. (2003). The lower bound for decelerating rates (as assumed in Early Burst) could be fixed as log(min.rate) / T, where T is the depth of the tree and min.rate is the minimum rate that could be assumed for the model (as proposed by Slater and Pennell (in press))

References

Blomberg S.P., Garland T.J., Ives A.R. 2003. Testing for phylogenetic signal in comparative data: behavioral traits are more labile. Evolution. 57:717-745. Harmon L.J., Losos J.B., Davies J.T., Gillespie R.G., Gittleman J.L., Jennings B.W., Kozak K.H., McPeek M.A., Moreno-Roark F., Near T.J., Purvis A., Ricklefs R.E., Schluter D., Schulte II J.A., Seehausen O., Sidlauskas B.L., Torres-Carvajal O., Weir J.T., Mooers A.O. 2010. Early bursts of body size and shape evolution are rare in comparative data. Evolution. 64:2385-2396. Slater G.J., Pennell M. 2013. Robust regression and posterior predictive simulation increase power to detect early bursts of trait evolution. Syst. Biol. in press.

See Also

mvMORPH mvOU mvBM mvSHIFT optim

Examples

## Toy Exemple
  set.seed(123)
  
  # Generating a random tree
  tree<-pbtree(n=25)
  tree2<-tree
  
  # Transform branch length according to an early burst 
  # Not run!
  #library(geiger)
  #tree2=exponentialchangeTree(tree, a=-1)

  # 2 Random traits evolving along the phylogeny
  data<-data.frame(head.size=rTraitCont(tree2), mouth.size=rTraitCont(tree2))

## Run the analysis!!
  # EB model with two traits
  mvEB(tree,data)

  # Comparison with BM model
  mvBM(tree,data,model="BM1",simmap.tree=FALSE)