CorIndex.groupEqual: Calculated the group-equalised Phi Coefficient of...
In Zabados/PhiCor: PhiCor: Calculating the Phi coefficient of Association

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

This function calculates the group-equalised version of the Phi coefficient of correlation Tichy and Chytry (2006), De Cáceres and Legendre (2009). This is useful alone to calculate the group-equalised version for a single habitat of interest among multiple other habitats.

1	CorIndex.groupEqual(CorIndexTargetVarInput)

CorIndexTargetVarInput

a custom class "CorIndexVar" containing all values from from both CorIndex and CorIndex.TargetVar

Phi - a floating point number between -1. and 1.

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

    #Builing on the examples from \link[PhiCor]{CorIndex.TargetVar}
    #Using the included dataframe Species1
    Inputs_species1 = CorIndex(InDataframe = Species1, speciesbinary = 'Species1', weighted = 'Proportion', group = 'HabId', 'LocationID')
    Inputs_species1$nkoverNk
    #Using CorIndex.TargetVar, the input is the output of CorIndex. In this case we are interested in
    #habitat 1 (using the ID for the habitat)
    Inputs_species1_hab1 = CorIndex.TargetVar(Inputs_species1, 1)

    species1_hab1_GE_Phi = CorIndex.groupEqual(Inputs_species1_hab1)



    #Using the included dataframe Species2
    #If you wanted the analysis to be non-weighted.
    Species2Unweight = Species2[which(Species2$Proportion==1),]
    #This analysis is then the unweighted version of the analysis. SquareID is not needed.
    Inputs_species2 =  CorIndex(InDataframe = Species2Unweight, speciesbinary = 'Species2', weighted = 'Proportion', group = 'HabId')
    names(Inputs_species2)

    #Looking at habitat 4
    Inputs_species2_hab4 = CorIndex.TargetVar(Inputs_species2, 4)

    species2_hab4_GE_Phi = CorIndex.groupEqual(Inputs_species2_hab4)