pco {ecodist} | R Documentation |
Principal coordinates analysis (classical scaling).
pco(x, negvals = "zero", dround = 0)
x |
a lower-triangular dissimilarity matrix. |
negvals |
if = "zero" sets all negative eigenvalues to zero; if = "rm" corrects for negative eigenvalues using method 1 of Legendre and Anderson 1999. |
dround |
if greater than 0, attempts to correct for round-off error by rounding to that number of places. |
PCO (classical scaling, metric multidimensional scaling) is very similar to principal components analysis, but allows the use of any dissimilarity metric.
values |
eigenvalue for each component. This is a measure of the variance explained by each dimension. |
vectors |
eigenvectors. Each column contains the scores for that dimension. |
Sarah Goslee
data(iris)
iris.d <- dist(iris[,1:4])
iris.pco <- pco(iris.d)
# scatterplot of the first two dimensions
plot(iris.pco$vectors[,1:2], col=as.numeric(iris$Species),
pch=as.numeric(iris$Species), main="PCO", xlab="PCO 1", ylab="PCO 2")