This is the Anolis bimaculatus dataset used in Butler & King (2004). It is used to test a hypothesis of character displacement using an interspecific dataset of body sizes and current data on sympatry/allopatry.
A data frame with 45 observations on the following 11 variables.
node: Labels for the nodes.
spcode: Two-letter code for each taxon.
species: Species names for extant species.
island: Name of the island on which the population is found.
size: Body size (head length in mm) of extant species.
ancestor: Ancestral node.
time: Time of node.
OU.1: a factor with levels
OU.3: a factor with levels
OU.4: a factor with levels
OU.LP: a factor with levels
Explanations of the data follow:
Body size. We use the phenotypic data and phylogeny of Losos (1990), which employed the head lengths (of males) as a proxy for body size. In this group of lizards, head length correlates very strongly with snout-to-vent length and the cube root of mass, which are standard measures of body size. The data are head lengths in mm; note that we use the log of this value in analyses.
The phylogenetic tree is encoded via three variables:
node variable gives a name to each node.
ancestor variable names the ancestor of each node.
The root node has no ancestor (i.e.,
time specifies the temporal location of each node, the root node being at time 0.
Specifications of selective regimes.
These columns are factors, the levels of which correspond to the “paintings” of the respective adaptive regime hypotheses onto the phylogeny (see
Each selective regime is named (small, medium, large, etc.).
Each column corresponds to a different painting of the selective regimes, and thus to a different hypothesis.
In this example, there are 3 alternative models (see Butler & King 2004):
OU.4 is 4-regime model,
OU.3 is 3-regime model (all ancestors are medium),
OU.LP is the linear parsimony model.
In addition to the above, there is a two-letter code for each taxon (
spcode) and the name of the island on which the taxon is found (
Marguerite A. Butler and Aaron A. King
## Analysis of Anolis bimaculatus data ## save time for CRAN tree <- with(bimac,ouchtree(node,ancestor,time/max(time),spcode)) plot(tree,node.names=TRUE) h1 <- brown(log(bimac['size']),tree) h1 plot(h1) h2 <- hansen(log(bimac['size']),tree,bimac['OU.1'],sqrt.alpha=1,sigma=1) h2 plot(h2) h3 <- hansen(log(bimac['size']),tree,bimac['OU.3'],sqrt.alpha=1,sigma=1) h3 plot(h3) h4 <- hansen(log(bimac['size']),tree,bimac['OU.4'],sqrt.alpha=1,sigma=1) h4 plot(h4) h5 <- hansen(log(bimac['size']),tree,bimac['OU.LP'],sqrt.alpha=1,sigma=1,reltol=1e-5) h5 <- update(h5,method='subplex',reltol=1e-11,parscale=c(0.1,0.1),hessian=TRUE) h5 plot(h5) simdat <- simulate(h5,nsim=10) hsim <- update(h5,data=simdat[]) summary(hsim) bsim <- update(h1,data=simdat[]) summary(bsim)
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