cati (version 0.8)

com.index.multi: Computing multitraits metrics to test and quantify the non-random assembly of communities

Description

Computing multitraits metrics to test and quantify the non-random assembly of communities

Usage

com.index.multi(traits = NULL, index = NULL, by.factor = NULL, 
	namesindex = NULL, nullmodels = NULL, ind.plot = NULL, sp = NULL, 
	com=NULL, reg.pool=NULL, nperm = 99, printprogress = TRUE, 
	ind.value=TRUE, type="count")

Arguments

traits

Individual Matrix of traits with traits in column.

index

A vector of functions to apply to traits vectors in the form "mean(x, na.rm = TRUE)" or "range(x)".

by.factor

A factor to split the Matrix of traits and compute index for each subset eg for each site.

namesindex

A vector of names for metrics.

nullmodels

A vector of values for null models coresponding to index. A value of 1 correspond to randomization of individual values within community. A value of 2 correspond to randomization of individual values within region. A values of 3 correspond to randomizatio

ind.plot

Factor defining the name of the plot (site or community) in which the individual is.

sp

Factor defining the species which the individual belong to.

com

Community data matrix with species (or populations) in rows and sites in column. Use only if ind.value=FALSE. "traits" matrix and "com" matrix must have the same number of rows.

reg.pool

Regional pool data for traits. If not informed, traits is considere as the regional pool. This matrix need to be larger than traits. Use only for null model 2.

nperm

Number of permutations. If NULL, only observed values are returned;

printprogress

Logical value; print progress during the calcul or not.

ind.value

Do the data are from individual value. If not, an internal function transform the abundance data matrix of com into a individual like matrix to compute the function.

type

Only if ind.value=FALSE. Either "count" or "abundance". Use abundance when all values in the com matrix are not superior to one.

Value

• A list of lists:
• $obs
  List of observed values for each trait in each community. Each component of the list correspond to a matrix containing the result for each custom function.
• $Null
  List of null values for each trait in each community. Each component of the list correspond to an array containing the result of the permutations for each custom function.
• $list.index
  List of index values and associate null models. Internal use in other function. Traits in columns.
• $list.index.t
  List of index values and associate null models. Internal use in other function. Traits in rows.
• $sites_richness
  Number of species per site.
• $namestraits
  Names of traits.

See Also

com.index; plot.listofindex; ses

Examples

data(finch.ind)
	
	####
	#For most multivariate functions we need to replace (or exclude)
	#NA values.
	
	#For this example, we use the package mice to complete the data.
	
	names.sp_ind_plot<-as.factor(paste(sp.finch, ind.plot.finch, sep="_")) 
		
		comm<-t(table(ind.plot.finch,1:length(ind.plot.finch)))
		
		library(mice)
		traits=traits.finch
		mice<-mice(traits.finch)
		traits.finch.mice<-complete(mice)
		
		####
		#A simple example to illustrate the concept of the function 
		#com.index.multi
		
		res.sum.1<-com.index.multi(traits.finch, 
		index=c("sum(scale(x), na.rm=TRUE)", "sum(x, na.rm=TRUE)"), 
		by.factor=names.sp_ind_plot, nullmodels=c(2,2), 
		ind.plot=ind.plot.finch, nperm=50, sp=sp.finch)
		
		attributes(ses.listofindex(as.listofindex(res.sum.1)))
		
		####
		#A more interesting example using the function hypervolume 
		#from the package hypervolume. 
		#We show here several results which differe in there factor 
		#that delimit the group to calculate different hypervolume 
		#(argument by_factor). 
		
		require(hypervolume)
		
		res.hv.1<-com.index.multi(traits.finch.mice, index=c("as.numeric
		(try(hypervolume(na.omit(x), reps=100, bandwidth=0.2, verbose=FALSE, 
		warnings=FALSE)@Volume))"), by.factor=rep(1,length(names.sp_ind_plot)), 
		nullmodels=c(2,2), ind.plot=ind.plot.finch, nperm=9, sp=sp.finch)
		
		res.hv.2<-com.index.multi(traits.finch.mice, index=c("as.numeric
		(try(hypervolume(na.omit(x), reps=100, bandwidth=0.2, verbose=FALSE, 
		warnings=FALSE)@Volume))"), by.factor=names.sp_ind_plot, 
		nullmodels=c(2,2), ind.plot=ind.plot.finch, nperm=9, sp=sp.finch)
		
		res.hv.3<-com.index.multi(traits.finch.mice, index=c("as.numeric
		(try(hypervolume(na.omit(x), reps=100, bandwidth=0.2, verbose=FALSE, 
		warnings=FALSE)@Volume))"), by.factor=ind.plot.finch, 
		nullmodels=c(2,2), ind.plot=ind.plot.finch, nperm=9, sp=sp.finch)
		
		res.hv.4<-com.index.multi(traits.finch.mice, index=c("as.numeric
		(try(hypervolume(na.omit(x), reps=100, bandwidth=0.2, verbose=FALSE, 
		warnings=FALSE)@Volume))"), by.factor=sp.finch, 
		nullmodels=c(2,2), ind.plot=ind.plot.finch, nperm=9, sp=sp.finch)
		
		list.ind.multi<-as.listofindex(list(res.hv.2, res.hv.3, res.hv.4))
		
		ses.listofindex(list.ind.multi)
		
		plot(list.ind.multi)
		plot(list.ind.multi, xlim=c(-200,20))