phangorn (version 2.1.1)

pmlMix: Phylogenetic mixture model

Description

Phylogenetic mixture model.

Usage

pmlMix(formula, fit, m=2, omega=rep(1/m, m), control=pml.control(epsilon=1e-08,  maxit=20, trace=1),...)

Arguments

formula

a formula object (see details).

fit

an object of class pml.

m

number of mixtures.

omega

mixing weights.

control

A list of parameters for controlling the fitting process.

...

Further arguments passed to or from other methods.

Value

pmlMix returns a list with elements

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 mixtures, on the right the parameter which are optimized specific to each mixture. The parameters available are "nni", "bf", "Q", "inv", "shape", "edge", "rate". Each parameters can be used only once in the formula. "rate" and "nni" are only available for the right side of the formula. On the other hand parameters for invariable sites are only allowed on the left-hand side. The convergence of the algorithm is very slow and is likely that the algorithm can get stuck in local optima.

See Also

pml,pmlPart,pmlCluster

Examples

## Not run: 
# X <- allSitePattern(5)
# tree <- read.tree(text = "((t1:0.3,t2:0.3):0.1,(t3:0.3,t4:0.3):0.1,t5:0.5);")
# fit <- pml(tree,X, k=4)
# weights <- 1000*exp(fit$site) 
# attr(X, "weight") <- weights
# fit1 <- update(fit, data=X, k=1)
# fit2 <- update(fit, data=X)
# 
# (fitMixture <- pmlMix(edge~rate, fit1 , m=4))
# (fit2 <- optim.pml(fit2, optGamma=TRUE))
# 
# 
# data(Laurasiatherian)
# dm <- dist.logDet(Laurasiatherian)
# tree <- NJ(dm)
# fit=pml(tree, Laurasiatherian)
# fit = optim.pml(fit)
# 
# fit2 <- update(fit, k=4)
# fit2 <- optim.pml(fit2, optGamma=TRUE)
# 
# fitMix = pmlMix(edge ~ rate, fit, m=4)
# fitMix
# 
# 
# #
# # simulation of mixture models 
# #
# \dontrun{
# X <- allSitePattern(5)
# tree1 <- read.tree(text = "((t1:0.1,t2:0.5):0.1,(t3:0.1,t4:0.5):0.1,t5:0.5);")
# tree2 <- read.tree(text = "((t1:0.5,t2:0.1):0.1,(t3:0.5,t4:0.1):0.1,t5:0.5);")
# tree1 <- unroot(tree1)
# tree2 <- unroot(tree2)
# fit1 <- pml(tree1,X)
# fit2 <- pml(tree2,X)
# 
# weights <- 2000*exp(fit1$site) + 1000*exp(fit2$site)
# attr(X, "weight") <- weights
# 
# fit1 <- pml(tree1, X)
# fit2 <- optim.pml(fit1)
# logLik(fit2)
# AIC(fit2, k=log(3000))
# 
# fitMixEdge = pmlMix( ~ edge, fit1, m=2)
# logLik(fitMixEdge)
# AIC(fitMixEdge, k=log(3000))
# 
# fit.p <- pmlPen(fitMixEdge, .25)
# logLik(fit.p)
# AIC(fit.p, k=log(3000))
# }
# ## End(Not run)