multispati: Multivariate spatial analysis

multispatiR Documentation

Multivariate spatial analysis


These functions are deprecated. See the function multispati and the methods plot.multispati, summary.multispati and print.multispati in the package adespatial.

This function ensures a multivariate extension of the univariate method of spatial autocorrelation analysis. By accounting for the spatial dependence of data observations and their multivariate covariance simultaneously, complex interactions among many variables are analysed. Using a methodological scheme borrowed from duality diagram analysis, a strategy for the exploratory analysis of spatial pattern in the multivariate is developped.


multispati(dudi, listw, scannf = TRUE, nfposi = 2, nfnega = 0)
## S3 method for class 'multispati'
plot(x, xax = 1, yax = 2, ...) 
## S3 method for class 'multispati'
summary(object, ...) 
## S3 method for class 'multispati'
print(x, ...)



an object of class dudi for the duality diagram analysis


an object of class listw for the spatial dependence of data observations


a logical value indicating whether the eigenvalues bar plot should be displayed


an integer indicating the number of kept positive axes


an integer indicating the number of kept negative axes

x, object

an object of class multispati

xax, yax

the numbers of the x-axis and the y-axis


further arguments passed to or from other methods


This analysis generalizes the Wartenberg's multivariate spatial correlation analysis to various duality diagrams created by the functions (dudi.pca, dudi.coa, dudi.acm, dudi.mix...) If dudi is a duality diagram created by the function dudi.pca and listw gives spatial weights created by a row normalized coding scheme, the analysis is equivalent to Wartenberg's analysis.

We note X the data frame with the variables, Q the column weights matrix and D the row weights matrix associated to the duality diagram dudi. We note L the neighbouring weights matrix associated to listw. Then, the 'multispati' analysis gives principal axes v that maximize the product of spatial autocorrelation and inertia of row scores :

I(XQv)*\|\|XQv\|\|^2 = t(v)t(Q)t(X)DLXQv


Returns an object of class multispati, which contains the following elements :


a numeric vector containing the eigenvalues


integer, number of kept axes associated to positive eigenvalues


integer, number of kept axes associated to negative eigenvalues


principle axes (v), data frame with p rows and (nfposi + nfnega) columns


principal components (XQv), data frame with n rows and (nfposi + nfnega) columns


lag vector onto the principal axes (LXQv), data frame with n rows and (nfposi + nfnega) columns


principal axes of the dudi analysis (u) onto principal axes of multispati (t(u)Qv), data frame with nf rows and (nfposi + nfnega) columns


Daniel Chessel
Sebastien Ollier
Thibaut Jombart


Dray, S., Said, S. and Debias, F. (2008) Spatial ordination of vegetation data using a generalization of Wartenberg's multivariate spatial correlation. Journal of vegetation science, 19, 45–56.

Grunsky, E. C. and Agterberg, F. P. (1988) Spatial and multivariate analysis of geochemical data from metavolcanic rocks in the Ben Nevis area, Ontario. Mathematical Geology, 20, 825–861.

Switzer, P. and Green, A.A. (1984) Min/max autocorrelation factors for multivariate spatial imagery. Tech. rep. 6, Stanford University.

Thioulouse, J., Chessel, D. and Champely, S. (1995) Multivariate analysis of spatial patterns: a unified approach to local and global structures. Environmental and Ecological Statistics, 2, 1–14.

Wartenberg, D. E. (1985) Multivariate spatial correlation: a method for exploratory geographical analysis. Geographical Analysis, 17, 263–283.

Jombart, T., Devillard, S., Dufour, A.-B. and Pontier, D. A spatially explicit multivariate method to disentangle global and local patterns of genetic variability. Submitted to Genetics.

See Also



## Not run: 
if (requireNamespace("spdep", quietly = TRUE)) {
    maf.xy <- mafragh$xy
    maf.flo <- mafragh$flo
    maf.listw <- spdep::nb2listw(neig2nb(mafragh$neig))
    if(adegraphicsLoaded()) {
      g1 <- s.label(maf.xy, nb = neig2nb(mafragh$neig), plab.cex = 0.75)
    } else {
      s.label(maf.xy, neig = mafragh$neig, clab = 0.75)
    maf.coa <- dudi.coa(maf.flo,scannf = FALSE) <- multispati(maf.coa, maf.listw, scannf = FALSE, nfposi = 2, nfnega = 2)
    ### detail eigenvalues components
    fgraph <- function(obj){
      # use multispati summary
      sum.obj <- summary(obj)
      # compute Imin and Imax
      L <- spdep::listw2mat(eval(as.list(obj$call)$listw))
      Imin <- min(eigen(0.5*(L+t(L)))$values)
      Imax <- max(eigen(0.5*(L+t(L)))$values)
      I0 <- -1/(nrow(obj$li)-1)
      # create labels
      labels <- lapply(1:length(obj$eig),function(i) bquote(lambda[.(i)]))
      # draw the plot
      xmax <- eval(as.list(obj$call)$dudi)$eig[1]*1.1
      var <- sum.obj[,2]
      moran <- sum.obj[,3]
      plot(x=var,y=moran,type='n',xlab='Inertia',ylab="Spatial autocorrelation (I)",
      ytick <- c(I0,round(seq(Imin,Imax,le=5),1))
      ytlab <- as.character(round(seq(Imin,Imax,le=5),1))
      ytlab <- c(as.character(round(I0,1)),as.character(round(Imin,1)),
      title("Spatial and inertia components of the eigenvalues")
    ## end eigenvalues details

    if(adegraphicsLoaded()) {
      g2 <- s1d.barchart(maf.coa$eig, p1d.hori = FALSE, plot = FALSE)
      g3 <- s1d.barchart($eig, p1d.hori = FALSE, plot = FALSE) 
      g4 <- s.corcircle($as, plot = FALSE)
      G1 <- ADEgS(list(g2, g3, g4), layout = c(1, 3))
    } else {
      par(mfrow = c(1, 3))
      par(mfrow = c(1, 1))
    if(adegraphicsLoaded()) {
      g5 <- s.value(maf.xy, -maf.coa$li[, 1], plot = FALSE)
      g6 <- s.value(maf.xy, -maf.coa$li[, 2], plot = FALSE)
      g7 <- s.value(maf.xy,$li[, 1], plot = FALSE)
      g8 <- s.value(maf.xy,$li[, 2], plot = FALSE)
      G2 <- ADEgS(list(g5, g6, g7, g8), layout = c(2, 2))
    } else {
      par(mfrow = c(2, 2))
      s.value(maf.xy, -maf.coa$li[, 1])
      s.value(maf.xy, -maf.coa$li[, 2])
      s.value(maf.xy,$li[, 1])
      s.value(maf.xy,$li[, 2])
      par(mfrow = c(1, 1))

    w1 <- -maf.coa$li[, 1:2]
    w1m <- apply(w1, 2, spdep::lag.listw, x = maf.listw) <-$li[, 1:2]
    w1.msm <- apply(, 2, spdep::lag.listw, x = maf.listw)
    if(adegraphicsLoaded()) {
      g9 <- s.match(w1, w1m, plab.cex = 0.75, plot = FALSE)
      g10 <- s.match(, w1.msm, plab.cex = 0.75, plot = FALSE)
      G3 <- cbindADEg(g9, g10, plot = TRUE)
    } else {
      par(mfrow = c(1,2))
      s.match(w1, w1m, clab = 0.75)
      s.match(, w1.msm, clab = 0.75)
      par(mfrow = c(1, 1))

    maf.pca <- dudi.pca(mafragh$env, scannf = FALSE)
    multispati.randtest(maf.pca, maf.listw) <- multispati(maf.pca, maf.listw, scannf=FALSE)

## End(Not run)

ade4 documentation built on Feb. 16, 2023, 7:58 p.m.

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