Demo -- An example for quick start.

You can get starting quickly with phyclust by using the demo() command in R. This demo will produce the three plots, and some of the results in the following.

R> library(phyclust)
R> demo("toy", package = "phyclust")

We have prepared a simulated data set seq.data.toy with 100 nucleotide sequences of length 200 sites from 4 clusters. The ancestral sequences were simulated using the HKY85 model (Hasegawa et al. 1985) along a tree of height 0.15 (expected number of mutations per site). The observed sequences were simulated along independent trees with height 0.09 descending from the ancestors.

	Each row represents a sequence and each column represents a site. The chosen sequence is fully colored, with green, blue, purple and red representing nucleotides A, G, C, and T. All other sequences show only mutant sites compared to the consensus sequence drawn in the first row. The dashed lines separate the clusters. The bottom row indicates the segregating sites, i.e. those sites containing at least one mutation.
	This shows the number of mutations of all sequences within each cluster relative to the chosen reference sequence. The top plot is for the whole data set. The other plots are for the four clusters.
	The resulting tree with branches colored according to the clusters defined in argument X.class. These clusters may be provided by the user (as is the case here) or as a result of inferring the clusters using phyclust().

This gives an example of the function phyclust() with changed option by .EMControl().

> EMC.2 <- .EMControl(init.procedure = "emEM")
> set.seed(1234)
> (ret.2 <- phyclust(X, 4, EMC = EMC.2))
Phyclust Results:
code type: NUCLEOTIDE, em method: EM, boundary method: ADJUST.
init procedure: emEM, method: randomMu.
model substitution: JC69, distance: D_JC69.
iter: 103 8725 0, convergence: 0, check.param: 1.
eps: 2.753e-14, error: 0.
N.X.org: 100, N.X.unique: 87, L: 200, K: 4, p: 804, N.seg.site: 127.
logL: -1379, bic: 6461, aic: 4367, icl: 6469
identifier: EE
  Eta: 0.2700 0.1898 0.2801 0.2602 
  Tt: 0.003074 
  n.class: 27 19 28 26
> RRand(ret.2$class.id, X.class)
   Rand adjRand  Eindex 
 1.0000  1.0000  0.1209

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