fix in read.dna and change in write.dna

[ape.git] / man / pcoa.Rd

diff --git a/man/pcoa.Rd b/man/pcoa.Rd
index 8e2137be98679506d0057ade9fb06dbaa3379e00..c5e43eaac33e76b0e7da265a7d1abb94aaaf659f 100644 (file)
--- a/man/pcoa.Rd
+++ b/man/pcoa.Rd
@@ -26,8 +26,6 @@ pcoa(D, correction="none", rn=NULL)
 \details{
 This function implements two methods for correcting for negative values in principal coordinate analysis (PCoA). Negative eigenvalues can be produced in PCoA when decomposing distance matrices produced by coefficients that are not Euclidean (Gower and Legendre 1986, Legendre and Legendre 1998).
 
-Another function in this library, \code{pcoa.all}, was developed to provide principal coordinate decomposition for the function \code{PCNM} of this library. It computes the eigenvectors corresponding to all eigenvalues, positive and negative, using matrix algebra.
-
 In \code{pcoa}, when negative eigenvalues are present in the decomposition results, the distance matrix D can be modified using either the Lingoes or the Cailliez procedure to produce results without negative eigenvalues.
 
 In the Lingoes (1971) procedure, a constant c1, equal to twice absolute value of the largest negative value of the original principal coordinate analysis, is added to each original squared distance in the distance matrix, except the diagonal values. A newe principal coordinate analysis, performed on the modified distances, has at most (n-2) positive eigenvalues, at least 2 null eigenvalues, and no negative eigenvalue.
@@ -36,7 +34,7 @@ In the Cailliez (1983) procedure, a constant c2 is added to the original distanc
 
 In all cases, only the eigenvectors corresponding to positive eigenvalues are shown in the output list. The eigenvectors are scaled to the square root of the corresponding eigenvalues. Gower (1966) has shown that eigenvectors scaled in that way preserve the original distance (in the D matrix) among the objects. These eigenvectors can be used to plot ordination graphs of the objects.
 
-We recommend not to use PCoA to produce ordinations from the chord, chi-square, abundance profile, or Hellinger distances. It is easier to first transform the community composition data using the following transformations,available in the \code{decostand} function of the \code{vegan} package, and then to carry out a principal component analysis (PCA) on the transformed data:
+We recommend not to use PCoA to produce ordinations from the chord, chi-square, abundance profile, or Hellinger distances. It is easier to first transform the community composition data using the following transformations, available in the \code{decostand} function of the \code{vegan} package, and then carry out a principal component analysis (PCA) on the transformed data:
 
 \describe{
 \item{ }{Chord transformation: decostand(spiders,"normalize") }
@@ -70,19 +68,19 @@ The \code{biplot.pcoa} function produces plots for any pair of principal coordin
 }
 
 \references{
-Cailliez, F. 1983. The analytical solution of the additive constant problem. \emph{Psychometrika}, \bold{48}, 305--308.
+Cailliez, F. (1983) The analytical solution of the additive constant problem. \emph{Psychometrika}, \bold{48}, 305--308.
 
-Gower, J. C. 1966. Some distance properties of latent root and vector methods used in multivariate analysis. \emph{Biometrika}, \bold{53}, 325--338.
+Gower, J. C. (1966) Some distance properties of latent root and vector methods used in multivariate analysis. \emph{Biometrika}, \bold{53}, 325--338.
 
-Gower, J. C. and P. Legendre. 1986. Metric and Euclidean properties of
+Gower, J. C. and Legendre, P. (1986) Metric and Euclidean properties of
 dissimilarity coefficients. \emph{Journal of Classification}, \bold{3}, 5--48.
 
-Legendre, P. and E. D. Gallagher. 2001. Ecologically meaningful transformations for ordination of species data. \emph{Oecologia}, \bold{129}, 271--280.
+Legendre, P. and Gallagher, E. D. (2001) Ecologically meaningful transformations for ordination of species data. \emph{Oecologia}, \bold{129}, 271--280.
 
-Legendre, P. and L. Legendre. 1998. \emph{Numerical ecology, 2nd English
+Legendre, P. and Legendre, L. (1998) \emph{Numerical Ecology, 2nd English
   edition.} Amsterdam: Elsevier Science BV.
 
-Lingoes, J. C. 1971. Some boundary conditions for a monotone analysis of symmetric matrices. \emph{Psychometrika}, \bold{36}, 195--203.
+Lingoes, J. C. (1971) Some boundary conditions for a monotone analysis of symmetric matrices. \emph{Psychometrika}, \bold{36}, 195--203.
 }
 
 \author{ Pierre Legendre, Universite de Montreal }
@@ -90,6 +88,7 @@ Lingoes, J. C. 1971. Some boundary conditions for a monotone analysis of symmetr
 \examples{
 # Oribatid mite data from Borcard and Legendre (1994)
 
+\dontrun{
 if (require(vegan)) {
 data(mite)  # Community composition data, 70 peat cores, 35 species
 
@@ -115,5 +114,6 @@ biplot(res, mite.log, dir.axis1=-1, dir.axis2=-1)
 biplot(res, mite.log.st, dir.axis1=-1, dir.axis2=-1)
 }
 }
+}
 
 \keyword{ multivariate }