A Rcpp implementation of various computations done on presence/absence matrices.
Installation
The simplest way to install this packages is to use the remotes package
install.package("remotes")
remotes::install_github("KevCaz/ecoocc")Once installed, load it and try it!
library(ecoocc)What is implemented so far?
Presence absence matrix
ec_as_pa() creates objects of class pa, there is a shortcut to randomly generate object of class pa quickly, ec_generate_pa().
# a pa object of 10 and 5 species all species having a prensence probability of .4
ec_generate_pa(10, 5, .4)
## ⚠ Empty site(s): 1, 2, 4
## ℹ Presence absence matrix: 10 sites, 5 species, 16 occurrences.
## spc_1 spc_2 spc_3 spc_4 spc_5
## sit_01 0 0 0 0 0
## sit_02 0 0 0 0 0
## sit_03 1 0 0 0 0
## sit_04 0 0 0 0 0
## sit_05 1 0 1 1 0
## sit_06 0 0 1 0 0
## sit_07 1 1 0 1 0
## sit_08 0 1 1 0 0
## sit_09 0 0 1 0 0
## sit_10 1 1 1 1 1Beta diversity
(mat <- ec_generate_pa(5, 3, .5))
## ⚠ Empty site(s): 1
## ℹ Presence absence matrix: 5 sites, 3 species, 9 occurrences.
ec_betadiversity(mat)
## spc_1 spc_2 spc_3
## sit_1 0 0 0
## sit_2 0 1 0
## sit_3 1 1 0
## sit_4 1 1 1
## sit_5 1 1 1
## site1 site2 bc
## 1 sit_1 sit_2 1.0000000
## 2 sit_1 sit_3 1.0000000
## 3 sit_1 sit_4 1.0000000
## 4 sit_1 sit_5 1.0000000
## 5 sit_2 sit_3 0.3333333
## 6 sit_2 sit_4 0.5000000
## 7 sit_2 sit_5 0.5000000
## 8 sit_3 sit_4 0.2000000
## 9 sit_3 sit_5 0.2000000
## 10 sit_4 sit_5 0.0000000Rarefaction
(mat <- ec_generate_pa(10, 4, .4))
## ⚠ Empty site(s): 6
## ℹ Presence absence matrix: 10 sites, 4 species, 12 occurrences.
ec_rarefaction(mat, 6)
## spc_1 spc_2 spc_3 spc_4
## sit_01 0 1 0 0
## sit_02 0 0 1 0
## sit_03 0 1 1 0
## sit_04 0 1 1 0
## sit_05 1 0 0 0
## sit_06 0 0 0 0
## sit_07 1 0 0 1
## sit_08 0 0 1 0
## sit_09 0 0 0 1
## sit_10 0 0 1 0
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 2 2 2 1 1 1
## [2,] 2 3 2 2 1 3
## [3,] 4 3 3 2 2 4
## [4,] 4 4 4 3 3 4
## [5,] 4 4 4 3 4 4
## [6,] 4 4 4 4 4 4
## [7,] 4 4 4 4 4 4
## [8,] 4 4 4 4 4 4
## [9,] 4 4 4 4 4 4
## [10,] 4 4 4 4 4 4Occurrence
mat <- rbind(c(0,0,1), c(0,1,0))
ec_cooc(ec_pa(mat))
## ⚠ No presence data for the following species: 1
## $cooc_count
## species1 species2 case_10 case_01 case_11 case_00 case_spc1 case_spc2
## 1 spc_1 spc_2 0 1 0 1 0 1
## 2 spc_1 spc_3 0 1 0 1 0 1
## 3 spc_2 spc_3 1 1 0 0 1 1
##
## $pairwise
## species1 species2 zs_bi zs_hy binary_covariance mean_pairwise_index
## 1 spc_1 spc_2 NaN NaN 0.0 NaN
## 2 spc_1 spc_3 NaN NaN 0.0 NaN
## 3 spc_2 spc_3 -0.8164966 -1 -0.5 0
## mutual_information c_score_unit overlap_1_2 overlap_2_1 symmetry
## 1 Inf 0 NaN 0 NaN
## 2 Inf 0 NaN 0 NaN
## 3 0.25 1 0 0 NaN
##
## $species
## species presence entropy robustness sensitivity
## 1 spc_1 0.0 NaN 0 NaN
## 2 spc_2 0.5 1 NaN 0
## 3 spc_3 0.5 1 NaN 0
##
## $global
## c_score c_score_S2
## 1 0.3333333 0.3333333R 📦
coocurPresenceAbsence-
EcoSimR(currently archived)