# dudi.pco: Principal Coordinates Analysis In ade4: Analysis of Ecological Data: Exploratory and Euclidean Methods in Environmental Sciences

 dudi.pco 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)
## S3 method for class 'pco'
scatter(x, xax = 1, yax = 2, clab.row = 1, posieig = "top",
sub = NULL, csub = 2, ...)
```

### Arguments

 `d` an object of class `dist` containing a 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
Anne-Béatrice Dufour anne-beatrice.dufour@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))
```

ade4 documentation built on April 19, 2022, 5:06 p.m.