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Principal Coordinates Analysis

pcoa.Rd

Computes classical (metric) multidimensional scaling.

Usage

pcoa(object, ...)

# S4 method for class 'dist'
pcoa(object, rank = 2)

Arguments

object

A distance structure.

...

Currently not used.

rank

An integer value specifying the maximal number dimension of the space which the data are to be represented in.

Value

A PCOA object.

References

Gower, J. C. (1966). Some Distance Properties of Latent Root and Vector Methods Used in Multivariate Analysis. Biometrika, 53(3‑4): 325-338. doi:10.1093/biomet/53.3-4.325 .

See also

stats::cmdscale()

Other multivariate analysis: ca(), mca(), pca(), predict()

Author

N. Frerebeau

Examples

## Load data
data("iris")

## Compute euclidean distances
d <- dist(iris[, 1:4], method = "euclidean")

## Compute principal coordinates analysis
X <- pcoa(d)

## Screeplot
screeplot(X)


## Plot results
plot(X, extra_quali = iris$Species)


## Add convex hulls
plot(
  x = X,
  extra_quali = iris$Species,
  hull = TRUE
)


## Add tolerance ellipses
plot(
  x = X,
  extra_quali = iris$Species,
  ellipse = list(type = "tolerance", level = 0.95)
)