aem: Construct asymmetric eigenvector maps (AEM)

In adespatial: Multivariate Multiscale Spatial Analysis

Construct asymmetric eigenvector maps (AEM)

Description

This function constructs eigenvectors of a site-by-link matrix. Weights can be applied to the links.

Usage

aem(
  aem.build.binary,
  binary.mat,
  weight,
  rm.link0 = FALSE,
  print.binary.mat = FALSE
)

Arguments

aem.build.binary	Object created by function aem.build.binary.
binary.mat	Site (n rows) by link (k columns) matrix. The 1s in the matrix represents the presence of a link influencing a site, directly or indirectly, otherwise the values are 0s.
weight	Vector of weights of length k, to be applied to the links.
rm.link0	Logical (TRUE, FALSE) determining if the links directly connecting a site to the origin (site 0) should be removed. Default value: FALSE. This parameter is only used when an object of class aem.build.binary is provided to the function.
print.binary.mat	Logical (TRUE, FALSE) determining if the site-by-link matrix used in the analysis should be printed.

Details

If only an object of class aem.build.binary is given to this function, The argument binary.mat is not considered. binary.mat is only considered when the argument aem.build.binary is missing.

If weights are applied to the links, the length of vector weight has to take into account wether the links connecting real sites to the origin (the fictitious site 0) have been kept or removed.

Value

value	A vector of singular values associated with the AEM.
vectors	A matrix of eigenvector. Each column is an AEM eigenfunction (or variable).
mod.binary.mat	A site-by-link matrix modified through the function.

Note

It sometimes happens that AEM eigenfunctions have equal singular values. In that case, different sets of AEM eigenfunctions may be produced on different plateforms.

Eigenvectors associated to an eigenvalue that is smaller than 10^{-12} are considered negligeable. They have been removed from the created AEM eigenfunctions.

Author(s)

F. Guillaume Blanchet

References

Blanchet F.G., P. Legendre and Borcard D. (2008) Modelling directional spatial processes in ecological data. Ecological Modelling, 215, 325-336.

See Also

aem.build.binary, svd

Examples

# Construction of object of class nb (spdep)
if(require("spdep", quietly = TRUE)){
nb <- cell2nb(5,5,"queen")

# Create fictitious geographical coordinates 
xy <- cbind(1:25,expand.grid(1:5,1:5))

# Build binary site-by-link matrix
bin.mat <- aem.build.binary(nb,xy)

# Construct AEM eigenfunctions from an object of class aem.build.binary
res <- aem(aem.build.binary=bin.mat,rm.link0=FALSE)
res$values

# Illustrate 4 AEM eigenfunctions using bubble plots
opal <- palette()
palette(c("black","white"))
oldpar <- par(mfrow=c(2,2))
symbols(x=xy[,2:3], circles=abs(res$vectors[,1]), inches=FALSE, asp=1,
 fg=ifelse(sign(-res$vectors[,1])+1>0,1,0), 
 bg=ifelse(sign(res$vectors[,1])+1>0,1,0), xlab="x", ylab="y")
title("AEM 1")
symbols(x=xy[,2:3], circles=abs(res$vectors[,2]), inches=FALSE, 
asp=1, fg=ifelse(sign(-res$vectors[,2])+1>0,1,0),
 bg=ifelse(sign(res$vectors[,2])+1>0,1,0), xlab="x", ylab="y")
title("AEM 2")
symbols(x=xy[,2:3], circles=abs(res$vectors[,3]), inches=FALSE, 
asp=1, fg=ifelse(sign(-res$vectors[,3])+1>0,1,0), 
bg=ifelse(sign(res$vectors[,3])+1>0,1,0), xlab="x", ylab="y")
title("AEM 3")
symbols(x=xy[,2:3], circles=abs(res$vectors[,4]), inches=FALSE, asp=1,
 fg=ifelse(sign(-res$vectors[,4])+1>0,1,0), 
 bg=ifelse(sign(res$vectors[,4])+1>0,1,0), xlab="x", ylab="y")
title("AEM 4")

# Construct AEM eigenfunctions using only a site-by-link matrix
res2 <- aem(binary.mat=bin.mat[[1]])
res2$values

# Illustrate 4 AEM eigenfunctions using bubble plots
par(mfrow=c(2,2))
symbols(x=xy[,2:3], circles=abs(res2$vectors[,1]), inches=FALSE, 
asp=1, fg=ifelse(sign(-res2$vectors[,1])+1>0,1,0), 
bg=ifelse(sign(res2$vectors[,1])+1>0,1,0), xlab="x", ylab="y")
title("AEM 1")
symbols(x=xy[,2:3], circles=abs(res2$vectors[,2]), inches=FALSE,
asp=1, fg=ifelse(sign(-res2$vectors[,2])+1>0,1,0), 
bg=ifelse(sign(res2$vectors[,2])+1>0,1,0), xlab="x", ylab="y")
title("AEM 2")
symbols(x=xy[,2:3], circles=abs(res2$vectors[,3]), inches=FALSE,
asp=1, fg=ifelse(sign(-res2$vectors[,3])+1>0,1,0), 
bg=ifelse(sign(res2$vectors[,3])+1>0,1,0), xlab="x", ylab="y")
title("AEM 3")
symbols(x=xy[,2:3], circles=abs(res2$vectors[,4]), inches=FALSE,asp=1,
 fg=ifelse(sign(-res2$vectors[,4])+1>0,1,0), 
 bg=ifelse(sign(res2$vectors[,4])+1>0,1,0), xlab="x", ylab="y")
title("AEM 4")

palette(opal)
par(oldpar)

# Construct AEM eigenfunctions with a function of the distance
# as weights to put on the links

# Construction of object of class nb (spdep)
nb<-cell2nb(5,5,"queen")

# Create fictitious geographical coordinates
xy <- cbind(1:25,expand.grid(1:5,1:5))

# Build binary site-by-link matrix
bin.mat <- aem.build.binary(nb,xy)

# Construct a matrix of distances
long.lien.mat<-as.matrix(dist(xy))

# Extract the edges, remove the ones directly linked to site 0
lien.b<-bin.mat$edges[-1:-5,]

# Construct a vector giving the length of each edge
long.lien<-vector(length=nrow(lien.b))

for(i in 1:nrow(lien.b)){
	long.lien[i]<-long.lien.mat[lien.b[i,1],lien.b[i,2]]
}

# Construct a vector of weights based on distance
weight.vec<-1-(long.lien/max(long.lien))^2

# Construct AEM eigenfunctions from an object of class aem.build.binary
res <- aem(aem.build.binary=bin.mat,weight=weight.vec,rm.link0=TRUE)
res

# Computing Moran's I for AEMs

# Building AEMs
xy <- cbind(1:25,expand.grid(1:5,1:5))
Wdist <- 1/as.matrix(dist(xy[,2:3]))

nb <- cell2nb(5,5,"queen")
bin.mat <- aem.build.binary(nb,xy)
linkBase <- bin.mat[[2]]
link <- linkBase[-which(linkBase[,1] == 0),]
weight <- numeric()

for(i in 1:nrow(link)){
   weight[i] <- Wdist[link[i,1],link[i,2]]
}

AEM <- aem(bin.mat, weight = weight, rm.link0 = TRUE)

# Constructing asymmetric matrix
matasym <- matrix(0,ncol=25, nrow=25)

for(i in 1:nrow(link)){
    matasym[link[i,1],link[i,2]]<- weight[i]
}

# Build a listw object from the asymmetric matrix
listwAsym <-  mat2listw(matasym, style = "B", zero.policy = TRUE)

# Calculate Moran's I for AEM
MoranIAEM <- moran.randtest(AEM$vectors, listwAsym)

}

adespatial documentation built on Sept. 11, 2024, 7:04 p.m.