Partition model.
Model to estimate phylogenies for partitioned data.
multiphyDat2pmlPart(x, rooted = FALSE, ...) pmlPart2multiPhylo(x) pmlPart(formula, object, control = pml.control(epsilon = 1e-08, maxit = 10, trace = 1), model = NULL, rooted = FALSE, ...)
x |
an object of class |
rooted |
Are the gene trees rooted (ultrametric) or unrooted. |
... |
Further arguments passed to or from other methods. |
formula |
a formula object (see details). |
object |
an object of class |
control |
A list of parameters for controlling the fitting process. |
model |
A vector containing the models containing a model for each partition. |
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
partitions, on the right the parameter which are optimized specific to each
partition. The parameters available are "nni", "bf", "Q", "inv",
"shape", "edge", "rate". Each parameters can be used only once in the
formula. "rate" is only available for the right side of the formula.
For partitions with different edge weights, but same topology, pmlPen
can try to find more parsimonious models (see example).
pmlPart2multiPhylo is a convenience function to extract the trees out
of a pmlPart object.
kcluster returns a list with elements
logLik |
log-likelihood of the fit |
trees |
a list of all trees during the optimization. |
object |
an object of class |
Klaus Schliep klaus.schliep@gmail.com
data(yeast) dm <- dist.logDet(yeast) tree <- NJ(dm) fit <- pml(tree,yeast) fits <- optim.pml(fit) weight=xtabs(~ index+genes,attr(yeast, "index"))[,1:10] sp <- pmlPart(edge ~ rate + inv, fits, weight=weight) sp ## Not run: sp2 <- pmlPart(~ edge + inv, fits, weight=weight) sp2 AIC(sp2) sp3 <- pmlPen(sp2, lambda = 2) AIC(sp3) ## End(Not run)
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.