Stochastic Partitioning
Stochastic Partitioning of genes into p cluster.
pmlCluster(formula, fit, weight, p = 1:5, part = NULL, nrep = 10, control = pml.control(epsilon = 1e-08, maxit = 10, trace = 1), ...)
formula |
a formula object (see details). |
fit |
an object of class |
weight |
|
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. |
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.
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 |
Klaus Schliep klaus.schliep@gmail.com
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
## Not run: 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) ## End(Not run)
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