R: Simulate sequences.

simSeq {phangorn}

R Documentation

Simulate sequences.

Description

Simulate sequences for a given evolutionary tree.

Usage

simSeq(x, ...)

## S3 method for class 'phylo'
simSeq(x, l = 1000, Q = NULL, bf = NULL, rootseq = NULL,
  type = "DNA", model = NULL, levels = NULL, rate = 1,
  ancestral = FALSE, ...)

## S3 method for class 'pml'
simSeq(x, ancestral = FALSE, ...)

Arguments

`x`	a phylogenetic tree `tree`, i.e. an object of class `phylo` or and object of class `pml`.
`...`	Further arguments passed to or from other methods.
`l`	length of the sequence to simulate.
`Q`	the rate matrix.
`bf`	base frequencies.
`rootseq`	a vector of length l containing the root sequence, other root sequence is randomly generated.
`type`	Type of sequences ("DNA", "AA" or "USER").
`model`	Amino acid models: e.g. "WAG", "JTT", "Dayhoff" or "LG"
`levels`	`levels` takes a character vector of the different bases, default is for nucleotide sequences, only used when type = "USER".
`rate`	mutation rate or scaler for the edge length, a numerical value greater than zero.
`ancestral`	Return ancestral sequences?

Details

simSeq is now a generic function to simulate sequence alignments. It is quite flexible and allows to generate DNA, RNA, amino acids or binary sequences. It is possible to give a pml object as input simSeq return a phyDat from these model. There is also a more low level version, which lacks rate variation, but one can combine different alignments having their own rate (see example). The rate parameter acts like a scaler for the edge lengths.

Value

simSeq returns an object of class phyDat.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

Examples


## Not run: 
data(Laurasiatherian)
tree = nj(dist.ml(Laurasiatherian))
fit = pml(tree, Laurasiatherian, k=4)
fit = optim.pml(fit, optNni=TRUE, model="GTR", optGamma=TRUE)
data = simSeq(fit)

## End(Not run)

tree = rtree(5)
plot(tree)
nodelabels()

# Example for simple DNA alignment
data = simSeq(tree, l = 10, type="DNA", bf=c(.1,.2,.3,.4), Q=1:6)
as.character(data)

# Example to simulate discrete Gamma rate variation
rates = discrete.gamma(1,4)
data1 = simSeq(tree, l = 100, type="AA", model="WAG", rate=rates[1])
data2 = simSeq(tree, l = 100, type="AA", model="WAG", rate=rates[2])
data3 = simSeq(tree, l = 100, type="AA", model="WAG", rate=rates[3])
data4 = simSeq(tree, l = 100, type="AA", model="WAG", rate=rates[4])
data <- c(data1,data2, data3, data4)

write.phyDat(data, file="temp.dat", format="sequential",nbcol = -1, colsep = "")
unlink("temp.dat")

[Package phangorn version 2.3.1 Index]