phangorn (version 2.0.3)

simSeq: 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="", 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.

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: one of "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

rate.

ancestral

Return ancestral sequences?

...

Further arguments passed to or from other methods.

Value

• simSeq returns an object of class phyDat.

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).

See Also

phyDat, pml, SOWH.test

Examples

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)

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")