Stochastic Partitioning

Stochastic Partitioning of genes into p cluster.

Usage

pmlCluster(formula, fit, weight, p = 1:5, part = NULL, nrep = 10,
  control = pml.control(epsilon = 1e-08, maxit = 10, trace = 1), ...)

Arguments

formula: a formula object (see details).
fit: an object of class pml.
weight: weight is matrix of frequency of site patterns for all genes.
p: number of clusters.
part: starting partition, otherwise a random partition is generated.
nrep: number of replicates for each p.
control: A list of parameters for controlling the fitting process.
...: Further arguments passed to or from other methods.

Value

pmlCluster returns a list with elements

logLik: log-likelihood of the fit
trees: a list of all trees during the optimization.
fits: fits for the final partitions

Details

The formula object allows to specify which parameter get optimized. The formula is generally of the form edge + bf + Q ~ rate + shape + ...{}, on the left side are the parameters which get optimized over all cluster, on the right the parameter which are optimized specific to each cluster. The parameters available are "nni", "bf", "Q", "inv", "shape", "edge", "rate". Each parameter can be used only once in the formula. There are also some restriction on the combinations how parameters can get used. "rate" is only available for the right side. When "rate" is specified on the left hand side "edge" has to be specified (on either side), if "rate" is specified on the right hand side it follows directly that edge is too.

References

K. P. Schliep (2009). Some Applications of statistical phylogenetics (PhD Thesis)

Lanfear, R., Calcott, B., Ho, S.Y.W. and Guindon, S. (2012) PartitionFinder: Combined Selection of Partitioning Schemes and Substitution Models for Phylogenetic Analyses. Molecular Biology and Evolution, 29(6), 1695-1701

Author

Klaus Schliep klaus.schliep@gmail.com

Examples


if (FALSE) { # \dontrun{
data(yeast)
dm <- dist.logDet(yeast)
tree <- NJ(dm)
fit <- pml(tree,yeast)
fit <- optim.pml(fit)

weight <- xtabs(~ index+genes,attr(yeast, "index"))
set.seed(1)

sp <- pmlCluster(edge~rate, fit, weight, p=1:4)
sp
SH.test(sp)
} # }