dudi.pco {ade4} | R Documentation

## Principal Coordinates Analysis

### Description

`dudi.pco` performs a principal coordinates analysis of a Euclidean distance matrix and returns the results as objects of class `pco` and `dudi`.

### Usage

    dudi.pco(d, row.w = "uniform", scannf = TRUE, nf = 2,
        full = FALSE, tol = 1e-07)
    scatter.pco (x, xax = 1, yax = 2, clab.row = 1, posieig = "top",
        sub = NULL, csub = 2, ...)

### Arguments

`d` | an object of class 'dist' containing an Euclidean distance matrix.
`row.w` | an optional distance matrix row weights. If not null, must be a vector of positive numbers with length equal to the size of the distance matrix
`scannf` | a logical value indicating whether the eigenvalues bar plot should be displayed
`nf` | if scannf FALSE, an integer indicating the number of kept axes
`full` | a logical value indicating whether all the axes should be kept
`tol` | a tolerance threshold to test whether the distance matrix is Euclidean : an eigenvalue is considered positive if it is larger than `-tol*lambda1` where `lambda1` is the largest eigenvalue.
`x` | an object of class `pco`
`xax` | the column number for the x-axis
`yax` | the column number for the y-axis
`clab.row` | a character size for the row labels
`posieig` | if "top" the eigenvalues bar plot is upside, if "bottom" it is downside, if "none" no plot
`sub` | a string of characters to be inserted as legend
`csub` | a character size for the legend, used with `par("cex")*csub`
`...` | further arguments passed to or from other methods

### Value

`dudi.pco` returns a list of class `pco` and `dudi`. See `dudi`

### Author(s)

Daniel Chessel chessel@biomserv.univ-lyon1.fr

### References

Gower, J. C. (1966) Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika, 53, 325–338.

### Examples

    data(yanomama)
    gen <- quasieuclid(as.dist(yanomama$gen))
    geo <- quasieuclid(as.dist(yanomama$geo))
    ant <- quasieuclid(as.dist(yanomama$ant))
    geo1 <- dudi.pco(geo, scann = FALSE, nf = 3)
    gen1 <- dudi.pco(gen, scann = FALSE, nf = 3)
    ant1 <- dudi.pco(ant, scann = FALSE, nf = 3)
    plot(coinertia(ant1, gen1, scann = FALSE))

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