README.md
In KevCaz/ecoocc: Occurrence, co-occurrence and more

ecoocc

A Rcpp implementation of various computations done on presence/absence matrices.

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)

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     1

(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 0.0000000

(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    4

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  0.3333333