| scores.listw {adespatial} | R Documentation | 
Function to compute and manage Moran's Eigenvector Maps (MEM) of a listw object
Description
These functions compute MEM (i.e., eigenvectors of a doubly centered spatial weighting matrix). Corresponding eigenvalues are linearly related to Moran's index of spatial autocorrelation.
Usage
scores.listw(
  listw,
  wt = rep(1, length(listw$neighbours)),
  MEM.autocor = c("non-null", "all", "positive", "negative"),
  store.listw = FALSE
)
mem(
  listw,
  wt = rep(1, length(listw$neighbours)),
  MEM.autocor = c("non-null", "all", "positive", "negative"),
  store.listw = FALSE
)
orthobasis.listw(
  listw,
  wt = rep(1, length(listw$neighbours)),
  MEM.autocor = c("non-null", "all", "positive", "negative"),
  store.listw = FALSE
)
## S3 method for class 'orthobasisSp'
x[i, j, drop = TRUE]
Arguments
| listw | An object of the class  | 
| wt | A vector of weights. It is used to orthogonalize the eigenvectors. It could be useful if MEM are used in weighted regression or canonical correspondence analysis | 
| MEM.autocor | A string indicating if all MEMs must be returned or only
those corresponding to non-null, positive or negative autocorrelation. The
difference between options  | 
| store.listw | A logical indicating if the spatial weighting matrix
should be stored in the attribute  | 
| x | An object of class  | 
| i,j | Elements to extract (integer or empty): index of rows (i) and columns (j). | 
| drop | A logical. If TRUE, object containing only one colum is converted in vector | 
Details
Testing the nullity of eigenvalues is based on E(i)/E(1) where E(i) is i-th eigenvalue and E(1) is the maximum absolute value of eigenvalues
Value
An object of class orthobasisSp , subclass orthobasis. 
The MEMs are stored as a data.frame. It contains several attributes 
(see ?attributes) including: 
-  values: The associated eigenvalues.
-  listw: The associated spatial weighting matrix (ifstore.listw = TRUE).
Author(s)
Stéphane Dray stephane.dray@univ-lyon1.fr
References
Dray, S., Legendre, P., and Peres-Neto, P. R. (2006). Spatial modeling: a comprehensive framework for principal coordinate analysis of neighbor matrices (PCNM). Ecological Modelling 196, 483–493.
Griffith D. A. (1996) Spatial autocorrelation and eigenfunctions of the geographic weights matrix accompanying geo-referenced data. Canadian Geographer 40, 351–367.
See Also
Examples
if(require("ade4", quietly = TRUE) & require("spdep", quietly = TRUE)){
data(oribatid)
nbtri <- tri2nb(as.matrix(oribatid$xy))
sc.tri <- scores.listw(nb2listw(nbtri, style = "B"))
summary(sc.tri)
}
if(require("adegraphics", quietly = TRUE)){
s.value(oribatid$xy,sc.tri[,1:9])
plot(sc.tri[,1:6], oribatid$xy, pSp.cex = 5, pSp.alpha = 0.5, pbackground.col = 'lightblue')
}