dcAncestralML {dcGOR}  R Documentation 
dcAncestralML
is supposed to reconstruct ancestral discrete
states using fast maximum likelihood algorithm. It takes inputs both
the phyloformatted tree and discrete states in the tips. The algorithm
assumes that state changes can be described by a probablistic
reversible model. It first determines transition matrix between states
(also considering branch lengths), then uses dynamic programming (from
tips to the root) to estimate conditional maximum likelihood, and
finally reconstructs the ancestral states (from the root to tips). If
the ties occur at the root, the state at the root is set to the last
state in ties (for example, usually being 'present' for
'present''absent' two states).
dcAncestralML(data, phy, transition.model = c("different", "symmetric", "same", "customised"), customised.model = NULL, edge.length.power = 1, initial.estimate = 0.1, output.detail = F, parallel = T, multicores = NULL, verbose = T)
data 
an input data matrix storing discrete states for tips (in rows) X characters (in columns). The rows in the matrix are for tips. If the row names do not exist, then addumedly they have the same order as in the tree tips. More wisely, users provide row names which can be matched to the tip labels of the tree. The row names can be more than found in the tree labels, and they should contain all those in the tree labels 
phy 
an object of class 'phylo' 
transition.model 
a character specifying the transition model. It can be: "different" for alltransitiondifferent model (such as matrix(c(0,1,2,0),2)), "symmetric" for the symmetric model (such as matrix(c(0,1,1,0),2) or matrix(c(0,1,2,1,0,3,2,3,0),3)), "same" for alltransitionsame model (such as matrix(c(0,1,1,0),2)), "customised" for the usercustomised model (see the next parameter) 
customised.model 
a matrix customised for the transition model. It can be: matrix(c(0,1,1,0),2), matrix(c(0,1,2,0),2), or matrix(c(0,1,2,1,0,3,2,3,0),3) 
edge.length.power 
a nonnegative value giving the exponent transformation of the branch lengths. It is useful when determining transition matrix between states 
initial.estimate 
the initial value used for the maximum likelihood estimation 
output.detail 
logical to indicate whether the output is returned as a detailed list. If TRUE, a nested list is returned: a list of characters (corresponding to columns of input data matrix), in which each element is a list consisting of three components ("states", "transition" and "relative"). If FALSE, a matrix is returned: the columns respond to the input data columns, and rows responding to all node index in the phyloformatted tree 
parallel 
logical to indicate whether parallel computation with
multicores is used. By default, it sets to true, but not necessarily
does so. Partly because parallel backends available will be
systemspecific (now only Linux or Mac OS). Also, it will depend on
whether these two packages "foreach" and "doMC" have been installed. It
can be installed via:

multicores 
an integer to specify how many cores will be registered as the multicore parallel backend to the 'foreach' package. If NULL, it will use a half of cores available in a user's computer. This option only works when parallel computation is enabled 
verbose 
logical to indicate whether the messages will be displayed in the screen. By default, it sets to TRUE for display 
It depends on the 'output.detail'. If FALSE (by default), a matrix is returned, with the columns responding to the input data columns, and rows responding to node index in the phyloformatted tree. If TRUE, a nested list is returned. Outermost list is for characters (corresponding to columns of input data matrix), in which each elemenl is a list (innermost) consisting of three components ("states", "transition" and "relative"):
states
: a named vector storing states (extant and
ancestral states)
transition
: an estimated transition matrix between states
relative
: a matrix of nodes X states, storing conditional
maximum likelihood being relative to each state
This fast dynamic programming for ancestral discrete state reconstruction is partially inspired by a joint estimation procedure as described in http://mbe.oxfordjournals.org/content/17/6/890.full
# 1) a newick tree that is imported as a phyloformatted tree tree < "(((t1:5,t2:5):2,(t3:4,t4:4):3):2,(t5:4,t6:4):6);" phy < ape::read.tree(text=tree) # 2) an input data matrix storing discrete states for tips (in rows) X four characters (in columns) data1 < matrix(c(0,rep(1,3),rep(0,2)), ncol=1) data2 < matrix(c(rep(0,4),rep(1,2)), ncol=1) data < cbind(data1, data1, data1, data2) colnames(data) < c("C1", "C2", "C3", "C4") ## reconstruct ancestral states, without detailed output res < dcAncestralML(data, phy, parallel=FALSE) res # 3) an input data matrix storing discrete states for tips (in rows) X only one character data < matrix(c(0,rep(1,3),rep(0,2)), ncol=1) ## reconstruct ancestral states, with detailed output res < dcAncestralML(data, phy, parallel=FALSE, output.detail=TRUE) res ## get the innermost list res < res[[1]] ## visualise the tree with ancestral states and their conditional probability Ntip < ape::Ntip(phy) Nnode < ape::Nnode(phy) color < c("white","gray") ## visualise main tree ape::plot.phylo(phy, type="p", use.edge.length=TRUE, label.offset=1, show.tip.label=TRUE, show.node.label=FALSE) ## visualise tips (state 1 in gray, state 0 in white) x < data[,1] ape::tiplabels(pch=22, bg=color[as.numeric(x)+1], cex=2, adj=1) ## visualise internal nodes ### thermo bar to illustrate relative probability (state 1 in gray, state 0 in white) ape::nodelabels(thermo=res$relative[Ntip+1:Nnode,2:1], piecol=color[2:1], cex=0.75) ### labeling reconstructed ancestral states ape::nodelabels(text=res$states[Ntip+1:Nnode], node=Ntip+1:Nnode, frame="none", col="red", bg="transparent", cex=0.75)