CorIndex.all.plusP: Calculated both the non and group-equalised Phi Coefficient...
In Zabados/PhiCor: PhiCor: Calculating the Phi coefficient of Association

Description Usage Arguments Details Value Author(s) References See Also Examples

This function calculates the non and group-equalised versions of the Phi coefficient of correlation Tichy and Chytry (2006), De Cáceres and Legendre (2009).

CorIndex.all.plusP(
  InDataframe,
  speciesbinary,
  weighted,
  group,
  numberIteration = 1000,
  SquareID = NULL,
  toroidal = FALSE
)

`InDataframe`	dataframe - containing the data on species presence absence, habitat and weighting. It also optionally could contain location ID's.
`speciesbinary`	string - the name of the binary field within InDataframe that contains binary presence absence for a species at the location.
`weighted`	string - name of habitat weighting field. For example, proportion of habitat, but any weighting with all habitats at a location adding to 1.
`group`	string - ID of habitats or groups of interest.
`numberIteration`	Integer - optional - the number of permutation, set as 1000 by default.
`SquareID`	string - optional if non-weighted, but MUST be used if a weighting is used. ID of each location. Useful later for permutating the locations to calculate p-values.
`toroidal`	Boolean - If you suspect that your data is spatially auto-correlated you should use a toroidal permutation.

It calculate p-values by permutation and therefore calculates take a bit of time. This is the main way of useing the PhiCor package and give full outputs.

a custom class "CorIndexVar" containing all values which are the same for all habitats and don't need to be recalculated for every habitat. Including Nk, nk, the habitats "GroupDF", N, K, Ngp, nkoverNk ng & n. See De Cáceres and Legendre (2009). To this is added another custom class "Output" as $'result' which gives the habitat ID, R (non group-equalised phi) pR (the p-value for R), Rg (group-equalised phi) and pRg (the p-value for Rg). This "Output" class is the main result, the CorIndexVar is included as metadata of the input values.

Jordan Chetcuti

Tichy, L., & Chytry, M. (2006). Statistical determination of diagnostic species for site groups of unequal size. Journal of Vegetation Science, 17(6), 809–818.

De Cáceres, M., & Legendre, P. (2009). Associations between species and groups of sites: indices and statistical inference. Ecology, 90(12), 3566–3574.

Chetcuti, J., Kunin, W. E. & Bullock, J. M. A weighting method to improve habitat association analysis: tested on British carabids. Ecography (Cop.). 42, 1395–1404 (2019).

PhiCor

    #Using the included dataframe Species1
    Species1_AllPhiPvalues = CorIndex.all.plusP(InDataframe = Species1, speciesbinary = "Species1", weighted = 'Proportion', group = 'HabId', SquareID = 'LocationID')
    names(Species1_AllPhiPvalues)
    Species1_AllPhiPvalues$`result`