msr.varipart {adespatial} | R Documentation |
The functions allows to evaluate the significance and estimate parts in variation partitioning using Moran Spectral Randomization (MSR) as a spatially-constrained null model to account for spatial autocorrelation in table X. Hence, this function provides a variation partioning adujsted for spurious correlation due to spatial autocorrelation in both the response and one explanatory matrix.
## S3 method for class 'varipart' msr( x, listwORorthobasis, nrepet = x$test$rep[1], method = c("pair", "triplet", "singleton"), ... )
x |
An object generated by the |
listwORorthobasis |
an object of the class |
nrepet |
an |
method |
an character specifying which algorithm should be used to produce spatial replicates (see codemsr.default). |
... |
further arguments of the codemsr.default function. |
The function corrects the biases due to spatial autocorrelation by using MSR procedure to produce environmental predictors that preserve the spatial autocorrelation and the correlation structures of the original environmental variables while being generated independently of species distribution.
An object of class varipart
randomized replicates.
(s) Stephane Dray stephane.dray@univ-lyon1.fr and Sylvie Clappe sylvie.clappe@univ-lyon1.fr
Clappe, S., Dray S. and P.R. Peres-Neto (2018) Beyond neutrality: disentangling the effects of species sorting and spurious correlations in community analysis. Ecology 99:1737-1747.
Wagner, H. H., and S. Dray (2015). Generating spatially constrained null models for irregularly spaced data using Moran spectral randomization methods. Methods in Ecology and Evolution 6:1169-1178.
library(ade4) library(spdep) data(mafragh) ## Performing standard variation partitioning dudiY <- dudi.pca(mafragh$flo, scannf = FALSE, scale = FALSE) mafragh.lw <- nb2listw(mafragh$nb) me <- mem(mafragh.lw, MEM.autocor = "positive") vprda <- varipart(dudiY, mafragh$env, me, type = "parametric") ## Adjust estimation and compute p-value by msr methods vprda.msr <- msr(vprda, mafragh.lw, nrepet=99) vprda.msr