ericmarcon / entropart

Entropy Partitioning to Measure Diversity

Entropy Partitioning to Measure Diversity

entropart is an R package that provides functions to calculate alpha, beta and gamma diversity of communities, including phylogenetic and functional diversity.

Estimation-bias corrections are available.

Details

In the entropart package, individuals of different species are counted in several communities which may (or not) be agregated to define a metacommunity. In the metacommunity, the probability to find a species in the weighted average of probabilities in communities. This is a naming convention, which may correspond to plots in a forest inventory or any data organized the same way.

Basic functions allow computing diversity of a community. Data is simply a vector of probabilities (summing up to 1) or of abundances (integer values that are numbers of individuals). Calculate entropy with functions such as Tsallis, Shannon, Simpson, Hurlbert or GenSimpson and explicit diversity (i.e. effective number of species) with Diversity and others. By default, the best available estimator of diversity will be used, according to the data.

Communities can be simulated by rCommunity, explicitely declared as a species distribution (as.AbdVector or as.ProbaVector), and plotted.

Phylogenetic entropy and diversity can be calculated if a phylogenetic (or functional), ultrametric tree is provided. See PhyloEntropy, Rao for examples of entropy and PhyloDiversity to calculate phylodiversity, with the state-of-the-art estimation-bias correction. Similarity-based diversity is calculated with Dqz, based on a similarity matrix.

Vignettes

A quick introduction is in vignette("entropart").

A full documentation is available online, in the "Articles" section of the web site of the vignette. It is a continuous update of the paper published in the Journal of Statistical Software (Marcon & Hérault, 2015).

The development version documentation is also available.

Reference

Marcon, E. and Herault, B. (2015). entropart: An R Package to Measure and Partition Diversity. Journal of Statistical Software. 67(8): 1-26.

entropart's Issues

DivPart - correction to non-integer values

Dear Eric,

I am trying to analyse diversity in microsat data using your package (I am interested in estimating genetic differentiation at the regional and population levels) and encountered the following 'problem': when using DivPart with biased=F and Correction="Best" settings, I get warnings that correction cannot be applied to non-integer Ns values, but the object created indicate that correction has been applied.

Here is a reproducable example:

library(entropart)
library(adegenet)
data(nancycats)
loclist<-seploc(nancycats)
df_gendat<-as(loclist[[2]], "data.frame") #we use only one locus (the second in this example)
df_gendat<-df_gendat[complete.cases(df_gendat),]
df_reduced<-df_gendat[1:180,]
pop_list<-c(rep("pop1",15),rep("pop2",25),rep("pop3",15),rep("pop4",25),rep("pop5",20),rep("pop6",20),rep("pop7",25),rep("pop8",15),rep("pop9",20)) #populations (9 populations in total)
reg_list<-c(rep("two",40),rep("three",60),rep("four",80)) #regions (3 regions in total; the first region consists of two populations, the second consists of three populations and the third consists of four populations, respectively)
df2_gendat<-cbind(reg_list,pop_list,df_reduced)
abun<-t(rowsum(df2_gendat[,3:ncol(df2_gendat)],df2_gendat$pop_list,na.rm=T))
two<-cbind(abun[,"pop1"],abun[,"pop2"]); colnames(two)<-c("pop1","pop2")
three<-cbind(abun[,"pop3"],abun[,"pop4"],abun[,"pop5"]); colnames(three)<-c("pop3","pop4","pop5")
four<-cbind(abun[,"pop6"],abun[,"pop7"],abun[,"pop8"],abun[,"pop9"]); colnames(four)<-c("pop6","pop7","pop8","pop9")
MC_two <- MetaCommunity(Abundances = two)
MC_three <- MetaCommunity(Abundances = three)
MC_four <- MetaCommunity(Abundances = four)
merged_total <- MergeMC(list(MC_two, MC_three, MC_four),Weights=c(2,3,4))
est_obs<-DivPart(q=1, merged_total, Biased=F, Correction="Best")
summary(est_obs)

Should I ignore these warnings? It may be related to unequal sample sizes (i.e. number of individuals) within populations - at least I do not get this warning when I set the number of individuals to be equal in all populations here.

Thank you for your help in advance!

Best wishes,
Zoltán