## S3 method for class 'DNAbin':
print(x, printlen = 6, digits = 3, \dots)
## S3 method for class 'DNAbin':
rbind(\dots)
## S3 method for class 'DNAbin':
cbind(\dots, check.names = TRUE, fill.with.gaps = FALSE,
quiet = FALSE)
## S3 method for class 'DNAbin':
[(x, i, j, drop = FALSE)
## S3 method for class 'DNAbin':
as.matrix(x, \dots)
## S3 method for class 'DNAbin':
c(\dots, recursive = FALSE)
## S3 method for class 'DNAbin':
as.list(x, \dots)
## S3 method for class 'DNAbin':
labels(object, \dots)
"DNAbin"
.print
, as.matrix
, and
labels
, or a series of objects of class "DNAbin"
in the
case of rbind
, c
check.names = FALSE
).TRUE
, the returned object is of the
lowest possible dimension."DNAbin"
in the case of rbind
,
cbind
, and [
."DNAbin"
. They are
used in the same way than the standard Rfunctions to manipulate
vectors, matrices, and lists. Additionally, the operators [[
and $
may be used to extract a vector from a list. Note that
the default of drop
is not the same than the generic operator:
this is to avoid dropping rownames when selecting a single sequence. These functions are provided to manipulate easily DNA sequences coded
with the bit-level coding scheme. The latter allows much faster
comparisons of sequences, as well as storing them in less memory
compared to the format used before
For cbind
, the default behaviour is to keep only individuals
(as indicated by the rownames) for which there are no missing data. If
fill.with.gaps = TRUE
, a `complete' matrix is returned,
enventually with insertion gaps as missing data. If check.names
= TRUE
(the default), the rownames of each matrix are checked, and
the rows are reordered if necessary. If check.names = FALSE
,
the matrices must all have the same number of rows, and are simply
binded; the rownames of the first matrix are used. See the examples.
as.matrix
may be used to convert DNA sequences (of the same
length) stored in a list into a matrix while keeping the names and the
class. as.list
does the reverse operation.
Paradis, E. (2012) Analysis of Phylogenetics and Evolution with R (Second Edition). New York: Springer.
as.DNAbin
, read.dna
,
read.GenBank
, write.dna
,
image.DNAbin
The corresponding generic functions are documented in the package
data(woodmouse)
woodmouse
print(woodmouse, 15, 6)
print(woodmouse[1:5, 1:300], 15, 6)
### Just to show how distances could be influenced by sampling:
dist.dna(woodmouse[1:2, ])
dist.dna(woodmouse[1:3, ])
### cbind and its options:
x <- woodmouse[1:2, 1:5]
y <- woodmouse[2:4, 6:10]
as.character(cbind(x, y)) # gives warning
as.character(cbind(x, y, fill.with.gaps = TRUE))
as.character(cbind(x, y, check.names = FALSE)) # gives an error
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