eigenmap-class {codep}

Class and Methods for Spatial Eigenvector Maps

Description

Create and handle spatial eigenvector maps of a set of locations a space with an arbitrary number of dimensions.

Usage

## S3 method for class 'eigenmap'
print(x, ...)

## S3 method for class 'eigenmap'
plot(x, ...)

Arguments

x

an 'eigenmap-class' object.

...

Further parameters to be passed to other functions or methods (currently ignored).

Format

'eigenmap-class' objects contain:

coordinates

A matrix of coordinates.

truncate

The interval within which pairs of sites are considered as neighbours.

D

A distance matrix.

weighting

The weighting function that had been used.

wpar

The weighting function parameter that had been used.

lambda

A vector of the eigenvalues obtain from the computation of the eigenvector map.

U

A matrix of the eigenvectors defining the eigenvector map.

Details

The 'print' method provides the number of the number of orthonormal variables (i.e. basis functions), the number of observations these functions are spanning, and their associated eigenvalues.

The 'plot' method provides a plot of the eigenvalues and offers the possibility to plot the values of variables for 1- or 2-dimensional sets of coordinates. plot.eigenmap opens the default graphical device driver, i.e., X11, windows, or quartz and recurses through variable with a left mouse click on the graphical window. A right mouse click interrupts recursing on X11 and windows (Mac OS X users should hit Esc on the quartz graphical device driver (Mac OS X users).

Functions

• print(eigenmap): Print method for eigenmap-class objects

• plot(eigenmap): Plot method for eigenmap-class objects

Author(s)

Guillaume Guenard and Pierre Legendre, Bertrand Pages Maintainer: Guillaume Guenard <[e-mail]>

References

Borcard, D. and Legendre, P. 2002. All-scale spatial analysis of ecological data by means of principal coordinates of neighbour matrices. Ecol. Model. 153: 51-68

Dray, S.; Legendre, P. and Peres-Neto, P. 2006. Spatial modelling: a comprehensive framework for principal coordinate analysis of neighbor matrices (PCNM). Ecol. Modelling 196: 483-493

Legendre, P. and Legendre, L. 2012. Numerical Ecology, 3rd English edition. Elsevier Science B.V., Amsterdam, The Netherlands.

See Also

MCA eigenmap