charadriiformes {dispRity}

Charadriiformes

Description

An example of a MCMCglmm model.

Format

one data.frame, one phylo and one MCMCglmm.

Details

This dataset is based on a random subset of 359 Charadriiformes (gulls, plovers and sandpipers) from Cooney et al 2017 and trees from Jetz et al 2012. It contains:

• data A "data.frame" .

• tree A consensus tree of 359 charadriiformes species ("phylo").

• posteriors The posteriors from a "MCMCglmm" model (see example below).

• tree_distribution A random distribution of 10 trees of the 359 charadriiformes species ("multiPhylo").

References

Cooney CR, Bright JA, Capp EJ, Chira AM,Hughes EC, Moody CJ, Nouri LO, Varley ZK, Thomas GH. Mega-evolutionary dynamics of the adaptive radiation of birds. Nature. 2017 Feb;542(7641):344-7.

Jetz W, Thomas GH, Joy JB, Hartmann K, Mooers AO. The global diversity of birds in space and time. Nature. 2012 Nov;491(7424):444-8.

Examples

## Not run: 
## Reproducing the MCMCglmm model
require(MCMCglmm)
data(charadriiformes)

## Setting up the model parameters:
## 1 - The formula (the first three PC axes)
model_formula <- cbind(PC1, PC2, PC3) ~ trait:clade-1
## 2 - The residual term
model_residuals <- ~us(trait):units
## 3 - The random terms
## (one per clade and one for the whole phylogeny)
model_randoms <- ~ us(at.level(clade,1):trait):animal
                 + us(at.level(clade,2):trait):animal
                 + us(at.level(clade,3):trait):animal
                 + us(trait):animal

## Flat priors for the residuals and random terms
flat_priors <- list(
     ## The residuals priors
     R = list(
         R1 = list(V = diag(3), nu = 0.002)), 
     ## The random priors (the phylogenetic terms)
     G = list(
         G1 = list(V = diag(3), nu = 0.002),
         G2 = list(V = diag(3), nu = 0.002),
         G3 = list(V = diag(3), nu = 0.002),
         G4 = list(V = diag(3), nu = 0.002)))

## Run the model for 110000 iterations
## sampled every 100 with a burnin (discard)
## of the first 10000 iterations)
model <- MCMCglmm(formula  = model_formula,
                  rcov     = model_residual,
                  random   = model_randoms,
                  family   = rep("gaussian", 3),
                  prior    = flat_priors,
                  nitt     = 110000,
                  burnin   = 10000,
                  thin     = 100,
                  pedigree = charadriiformes$tree,
                  data     = charadriiformes$data)

## End(Not run)