optim.likRatePhylo {motmot} | R Documentation |
Maximum likelihood rate estimation for traits and phylogenies
Description
Function for the maximum likelihood estimation of rate parameters on a trait and phylogeny.
Usage
optim.likRatePhylo(
rateData,
rate = NULL,
fixed = NULL,
rateMIN = 0.001,
rateMAX = 50,
common.mean = FALSE,
lambda.est = TRUE,
meserr = FALSE
)
Arguments
rateData |
an object of class |
rate |
a vector of relative rate parameters. The length of the vector is equal to the number of rates being estimated. If |
fixed |
A vector stating whether each parameter should be allowed to vary (either |
rateMIN |
Minimum value for the rate parameters |
rateMAX |
Maximum value for the rate parameters |
common.mean |
a logical specififying whether each rate category should have its own mean ( |
lambda.est |
Logical. Fit Pagel's lambda. |
meserr |
Logical. Include measurement error. |
Value
MLRate Maximum likelihood estimates of the rate parameters
Max.lik Maximum (log) likeihood
AIC AIC for maximum likelihood model
AICc AICc for maximum likelihood model
convergence convergence value from optim
n.parameters Number of parameters in the model (how many means and rate categories)
Author(s)
Gavin Thomas
References
Thomas GH, Freckleton RP, & Szekely T. 2006. Comparative analyses of the influence of developmental mode on phenotypic diversification rates in shorebirds. Proceedings of the Royal Society B 273, 1619-1624.
Thomas GH, Meiri S, & Phillimore AB. 2009. Body size diversification in Anolis: novel environments and island effects. Evolution 63, 2017-2030.
Examples
data(anolis.tree)
data(anolis.data)
## Convert data to class rateData with a rateMatrix object as input
anolis.rateMatrix <- as.rateMatrix(phy=anolis.tree, x="geo_ecomorph", data=anolis.data)
anolis.rateData <- as.rateData(y="Female_SVL", x="geo_ecomorph",
rateMatrix = anolis.rateMatrix, phy=NULL, data=anolis.data, log.y=TRUE)
# A model with a different rate in each of the four groups. The 'fixed' command is used to determine
# whether a particular rate is to be constrained or not. Use '1' to fix a group and 'FALSE' to show
# that the parameter is not fixed and should be estimated. The values should be entered in the same
# order as the ranking of the groups. That is, group 0 (small islands) takes position one in the
# fixed vector, group 1 (large island trunk crown and trunk ground) takes position 2 and so on.
# The default is to allow each group to take a different mean.
optim.likRatePhylo(anolis.rateData, rate=c(1,1,1,1), common.mean=TRUE, lambda.est=FALSE)