entropart Methods

The entropart package provides generic methods to measure diversity. S3 methods to aplly them to simulated communities are available here. AbdVector returns an abundance vector and ProbaVector returns a probability vector. Tsallis returns Tsallis's entropy of a community, Diversity its diversity. Richness, Shannon and Simpson return particular indices of diversity.

# S3 method for pattern_matrix_individuals
as.AbdVector(x, ...)

# S3 method for pattern_matrix_individuals
as.ProbaVector(x, ...)

# S3 method for pattern_matrix_individuals
Tsallis(NorP, q = 1, Correction = "Best", ..., CheckArguments = TRUE)

# S3 method for pattern_matrix_individuals
Diversity(NorP, q = 1, Correction = "Best", ..., CheckArguments = TRUE)

# S3 method for pattern_matrix_individuals
Richness(
  NorP,
  Correction = "Chao1",
  Alpha = 0.05,
  JackOver = FALSE,
  ...,
  CheckArguments = TRUE
)

# S3 method for pattern_matrix_individuals
Shannon(NorP, Correction = "Best", ..., CheckArguments = TRUE)

# S3 method for pattern_matrix_individuals
Simpson(NorP, Correction = "Lande", ..., CheckArguments = TRUE)

Arguments

x

An object of class pattern_matrix_individuals.

...

Further arguments. Unsused.

NorP

An object of class "wmppp" (pattern_matrix_individuals).

q

A number: the order of entropy. Some corrections allow only a positive number. Default is 1 for Shannon entropy.

Correction

A string containing one of the possible corrections: "None" (no correction), and "Best" are always valid. See the generic function help for other possibilities.

CheckArguments

If TRUE (default), the function arguments are verified. Should be set to FALSE to save time in simulations for example, when the arguments have been checked elsewhere.

Alpha

The risk level, 5% by default, used to optimize the jackknife order.

JackOver

If TRUE, retain the jackknife order immediately superior to the optimal one, usually resulting in the overestimation of the number of species. Default is FALSE.

Examples

# A community matrix drift model
myModel <- cm_drift$new(pattern_matrix_individuals(S=10))
myModel$autoplot()

as.AbdVector(myModel$pattern)
#>  1  3  4  5  6  7  8  9 10 
#> 13 20  6  7  2  3  9  3  1 
#> attr(,"class")
#> [1] "AbdVector"           "SpeciesDistribution" "array"              
as.ProbaVector(myModel$pattern)
#>        1        3        4        5        6        7        8        9 
#> 0.203125 0.312500 0.093750 0.109375 0.031250 0.046875 0.140625 0.046875 
#>       10 
#> 0.015625 
#> attr(,"class")
#> [1] "ProbaVector"         "SpeciesDistribution" "array"              
Tsallis(myModel$pattern)
#>  UnveilJ 
#> 1.937234 
Diversity(myModel$pattern)
#>  UnveilJ 
#> 6.939533 
Richness(myModel$pattern)
#>    Chao1 
#> 9.492188 
Shannon(myModel$pattern)
#>  UnveilJ 
#> 1.937234 
Simpson(myModel$pattern)
#>    Lande 
#> 0.827877