Dear Mohammad, as far as I know, there isn't a package that supports the Camargo evenness index. It's not that difficult to implement yourself though. Attached, you will find my attempt, hopefully it's helpful to you.
Dear Mohammad, as far as I know, there isn't a package that supports the Camargo evenness index. It's not that difficult to implement yourself though. Attached, you will find my attempt, hopefully it's helpful to you.
Hmm, the answer above given provides a sort of monte-carlo difference assessment against the individual species data-point. I see this type of reasoning increasingly in use in respect of population data but I am, myself, in a bit of a quandary in respect to what it means in an operational sense. It is a randomization of particular data-sets. Each data-set is a particular response but the resulting aggregate is still merely an equal-likelihood randomized sample - certainly embracing some kind of variance but of what? It is very tricky and I am loathe to use it. The argument resulting seems to embrace a great deal of circularity and was railed against by Ramon Margalef. Computers are tricky things - just because one can force them to digest numbers it does not mean that the ultimate consumer (us) will escape indigestion when eating the output.
If R is is the R I think it is then there is a theoretical absolutely even R that can be calculated as opposed to that enumerated by different observed representations by species. Absolute evenness is R calculated by assigning total number of individuals (counted in the overall sample) divided by the total number of species. This figure represents number of individuals for each of the species (R-evenness). R-observed is calculated on the observed values individuals/species. The difference (R-evenness) - (R-observed) is due to the "species-dominance" stress-factor. The limitations for comparison being imposed by the necessity for sample-space equivalence or identity of some form,
The camargo evenness is now implemented in the R microbiome package (maintained by me & available via github with install_github("microbiome/microbiome"), and can be calculated for a random poisson vector with: "library(microbiome); evenness(rpois(100,10), "camargo")".