adespatial (version 0.3-24)

mspa: Multi-Scale Pattern Analysis

Description

The multi-scale pattern analysis (MSPA, Jombart et al 2009) investigates the main scales of spatial variation in a multivariate dataset. This implementation allows one to perform a MSPA using any multivariate analysis (stored as a dudi object), and a list of spatial weights (class listw) or an object of class orthobasisSp.

Usage

mspa(
  dudi,
  lwORorthobasisSp,
  nblocks,
  scannf = TRUE,
  nf = 2,
  centring = c("param", "sim"),
  nperm = 999
)
# S3 method for mspa
scatter(
  x,
  xax = 1,
  yax = 2,
  posieig = "topleft",
  bary = TRUE,
  plot = TRUE,
  storeData = TRUE,
  pos = -1,
  ...
)
# S3 method for mspa
print(x, ...)

Value

An object having the classes mspa and

dudi: mspa objects are dudi

objects with the following extra slots:
- ls: principal components of the MSPA. These are the coordinates of variables onto principal axes, to be used for plotting. Correspond to matrix B in Appendix A of Jombart et al (2009).
- R2: matrix of R2 between variables and MEMs. Corresponds to S in Jombart et al (2009).
- meanPoint: coordinates of the 'mean variable' onto principal axes. The 'mean variable' is an hypothetic variable whose scale profile is the average of those of all variables of the analysed dataset.
- varweights: the weights of variables. Corresponds to d in Jombart et al. (2009).

Arguments

dudi

a duality diagram (i.e. a reduced space ordination) obtained by a dudi function (for instance dudi.pca).

lwORorthobasisSp

either a list of weights (class listw) that san be obtained easily using the function chooseCN or an object of class orthobasisSp

nblocks

an integer indicating the number of blocks to divide MEMs.

scannf

logical, indicating whether the screeplot should be displayed to choose the number or retained factors.

nf

the number of retained factors

centring

a character string indicating if parametric ("param") or non-parametric ("sim") centring should be used

nperm

an integer giving the number of permutations used to compute the theoretical coefficients of determination (999 by default); used if centring="sim".

x

a mspa object.

xax

an integer indicating the x axis to be displayed.

yax

an integer indicating the y axis to be displayed.

posieig

a character indicating the position of the screeplot (any of the four combination between "top", "bottom", "left" and "right").

bary

a logical indicating whether the barycenter of the variables should be displayed.

plot

a logical indicating if the graphics is displayed

storeData

a logical indicating if the data should be stored in the returned object. If FALSE, only the names of the data arguments are stored

pos

an integer indicating the position of the environment where the data are stored, relative to the environment where the function is called. Useful only if storeData is FALSE

...

additional graphical parameters (see adegpar and trellis.par.get)

Author

Thibaut Jombart

Details

The scatter method is used for plotting the results. Compared to the original version of the method, this new implementation allows to specify a number of blocks (nblocks). In this case, the multiscale decomposition is performed by dividing MEMs into several blocks and summing R2 values. This could facilitate the interpretation of results.

References

Jombart T, Dray S, and Dufour, A-B. (2009) Finding essential scales of spatial variation in ecological data: a multivariate approach. Ecography 32: 161-168.

See Also

chooseCN to obtain a list of spatial weights.

Examples

####################################
### using oribatib mites dataset ###
####################################

if(require("ade4", quietly = TRUE)){
## load data
data(oribatid)

## get the list of spatial weights
cn <- chooseCN(oribatid$xy, res = "listw", ask = FALSE, type = 1)

## Hellinger transformation
hellTrans <- function(X){
  if (!( is.matrix(X) | is.data.frame(X) )) stop("Object is not a matrix.")  
  if (any(is.na(X))) stop("na entries in table.")
  
  sumRow <- apply(X,1,sum)
  Y <- X/sumRow
  Y <- sqrt(Y)
  
  return(Y)
}


## ENVIRONMENTAL VARIABLES ##
## Hill and Smith analysis for environmental variables
## (for a mixture of quantitative / qualitative variables)
hsEnv <- dudi.hillsmith(oribatid$envir,scannf=FALSE)

## detrending of the analysis (residuals of regression onto xy coordinates)
hsEnv.detr <- pcaivortho(hsEnv,oribatid$xy,scannf=FALSE)

## MSPA of the detrended analysis
mspaEnv <- mspa(hsEnv.detr,cn,scannf=FALSE,nf=2)
scatter(mspaEnv)



## SPECIES DATA ##
## PCA of species abundances, after Hellinger transformation
pcaFau <- dudi.pca(hellTrans(oribatid$fau),scale=FALSE,scannf=FALSE)

## detrending of this PCA
pcaFau.detr <- pcaivortho(pcaFau,oribatid$xy,scannf=FALSE)

# MSPA of the detrended analysis
mspaFau <- mspa(pcaFau.detr,cn,scannf=FALSE,nf=2)
scatter(mspaFau)



## CANONICAL MSPA ##
## RDA species ~ envir
## (species abundances predicted by environment)
## note: RDA = 'PCAIV' (PCA with Instrumental Variables)
rda1 <- pcaiv(dudi=pcaFau.detr, df=oribatid$envir,scannf=FALSE,nf=2)

## canonical MSPA (species predicted by environment)
mspaCan1 <- mspa(dudi=rda1, lw=cn, scannf=FALSE, nf=2)
scatter(mspaCan1)

## same analysis, using a non-parametric centring
mspaCan1NP <- mspa(dudi=rda1, lw=cn, scannf=FALSE, nf=2,cent="sim",nper=999)
scatter(mspaCan1NP) # basically no change



## PARTIAL CANONICAL MSPA ##
## partial RDA species ~ envir
## (species abundances not predicted by environment)
rda2 <- pcaivortho(dudi=pcaFau.detr,df=oribatid$envir,scannf=FALSE,nf=2)

## partial canonical MSPA
mspaCan2 <- mspa(dudi=rda2, lw=cn, scannf=FALSE, nf=2)
scatter(mspaCan2) # nothing left
}