nullModel: SwapClass null model
In swapClass: A Null Model Adapted to Abundance Class Data in Ecology

Description Usage Arguments Details Value Author(s) References See Also Examples

a simple null model adapted to semi-quantitative multi-classes (or partially ordered) data.

1	nullModel(mat, nsim = 100, recursive = TRUE, burnin = NULL, thin = NULL, strata = NULL)

`mat`	an object of class matrix (or data.frame) with numerical abundance classes (site by row and species by column).
`nsim`	Number of simulated null communities (positive integer), default = 100.
`recursive`	A logical value indicating if generation of a new null community is performed from the last one created, defaut = T.
`burnin`	Number of null communities discarded before proper analysis (positive integer), if NULL (defaut) then burnin = nrow(mat)ncol(mat)10.
`thin`	Number of discarded null communities between two evaluations (positive integer) if recursive=T, if NULL (defaut) then thin = max(nrow(mat)ncol(mat)10,1000).
`strata`	a numeric vector of length egal to nrow(mat) supplying strata. Swap are performed within strata, defaut = NULL.

The SwapClass model is derived from the "swap philosophy" apply on presence-absence data. The limited number of abundance classes and their repetitiveness allow the extension of the "swap" method classically applied on presence-absence data with two classes (0 and 1). In the same way that "swap" methods permute sub-matrices of presence/absence community matrix, semi-quantitative data can be randomized by swapping sub-matrices while row and column marginals are not modified. nullModel calls a dynamically defined functions (swapClass_swapC) with in-lined C code compiled by setCMethod on package load.

`sim`	an object of class list containing the null communities
`perms`	an object of class list containing matrices with the number of times each cell has been swapped.

Benjamin Borgy (benjamin.borgy@gmail.com), Remi Perronne, Xavier Reboud

Borgy B. - Dynamic and assembly of weed communities: Approach by statistical modeling (2011). Ph.D. Thesis. INRA Agroicologie & Universiti de Bourgogne. DOI: 10.13140/RG.2.1.1738.1601

nbPerm

MAT=matrix(sample(0:3,50*20,replace=TRUE,prob=c(.7,.1,.1,.1)),ncol=20)

#Generation of null communities
NULL_MATS=nullModel(MAT)

#First null community
NULL_MATS$sim[[1]]

#Number of times that each cell has been swapped for the first null community
NULL_MATS$perms[[1]]

#nbPerm index over the observed community and the 100 null communities
plot(c(nbPerm(MAT),unlist(lapply(NULL_MATS$sim,nbPerm))),type='l',ylab="nbPerm index")

#the number of each classes per row are equal
#between observed community and the first null community
f_table = function(x) table(factor(x,levels=0:3))
all(apply(MAT,1,f_table)==apply(NULL_MATS$sim[[1]],1,f_table))

#the number of each classes per column are equal
#between observed community and the first null community
all(apply(MAT,2,f_table)==apply(NULL_MATS$sim[[1]],2,f_table))