X-Git-Url: https://git.donarmstrong.com/?a=blobdiff_plain;f=man%2FDNAbin.Rd;h=020f6f7fd1de26173aa897a8fa1adf8dd80d8945;hb=b9e04a6e6af3beda74b916eda00b42ac38875563;hp=bce0df2da8998c237f3a22dc7c78bd19627e1669;hpb=f295ab19440298e543db5a270e54f10a84382197;p=ape.git diff --git a/man/DNAbin.Rd b/man/DNAbin.Rd index bce0df2..020f6f7 100644 --- a/man/DNAbin.Rd +++ b/man/DNAbin.Rd @@ -42,7 +42,7 @@ are dropped.} \item{i, j}{indices of the rows and/or columns to select or to drop. They may be numeric, logical, or character (in the same way than for - standard R objects).} + standard \R objects).} \item{drop}{logical; if \code{TRUE}, the returned object is of the lowest possible dimension.} \item{recursive}{for compatibility with the generic (unused).} @@ -50,7 +50,7 @@ \details{ These are all `methods' of generic functions which are here applied to DNA sequences stored as objects of class \code{"DNAbin"}. They are - used in the same way than the standard R functions to manipulate + used in the same way than the standard \R functions to manipulate vectors, matrices, and lists. Additionally, the operators \code{[[} and \code{$} may be used to extract a vector from a list. Note that the default of \code{drop} is not the same than the generic operator: @@ -81,11 +81,15 @@ \references{ Paradis, E. (2007) A Bit-Level Coding Scheme for Nucleotides. \url{http://ape.mpl.ird.fr/misc/BitLevelCodingScheme_20April2007.pdf} + + Paradis, E. (2012) \emph{Analysis of Phylogenetics and Evolution with + R (Second Edition).} New York: Springer. } \author{Emmanuel Paradis} \seealso{ \code{\link{as.DNAbin}}, \code{\link{read.dna}}, - \code{\link{read.GenBank}}, \code{\link{write.dna}} + \code{\link{read.GenBank}}, \code{\link{write.dna}}, + \code{\link{image.DNAbin}} The corresponding generic functions are documented in the package \pkg{base}. @@ -93,9 +97,8 @@ \examples{ data(woodmouse) woodmouse -summary(woodmouse) -summary(woodmouse, 15, 6) -summary(woodmouse[1:5, 1:300], 15, 6) +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, ])