R/CorrelationIndex.R
In Zabados/PhiCor: PhiCor: Calculating the Phi coefficient of Association
Defines functions
FilePathInTree
Indicator.all.plusP
Indicator.xPerm
Indicator.groupEqual
Indicator.nonEqual
CorIndex.all.plusP
CorIndex.xPerm
CorIndex.toroidalAPerm
CorIndex.APerm
CorIndex.all
CorIndex.nonEqual
CorIndex.groupEqual
CorIndex.TargetVar
CorIndex.default
CorIndex
lookup
default.Output
print.Output
Documented in
CorIndex
CorIndex.all
CorIndex.all.plusP
CorIndex.default
CorIndex.groupEqual
CorIndex.nonEqual
CorIndex.TargetVar
CorIndex.xPerm
FilePathInTree
Indicator.all.plusP
Indicator.groupEqual
Indicator.nonEqual
Indicator.xPerm
lookup
#' Package for calculation the Weighted Phi Coefficient of association
#'
#' This package can be used to calculate the weighted phi coeficient of
#' association using a weighting for habitats within a location, or
#' can calculate the unweighted original version from
#' \cite{De Cáceres and Legendre (2009)} by using a weighting of one.
#' The package also allows the group-equalised version to be run.
#'
#' The Phi Coefficent of association gives the habitat preference, or
#' site fidelity of habitat sites.
#'
#' @details
#' The main function in PhiCor is \link[PhiCor]{CorIndex.all.plusP}.
#' This will calculate all phi corelation of association values and
#' purmutate to give p-values.
#'
#' The other Phi coefficent functions are useful in testing or calculating only part
#' of the full outputs from \link[PhiCor]{CorIndex.all.plusP}:
#' \itemize{
#' \item \link[PhiCor]{CorIndex} calculates the inputs that don't change between habitats.
#' \item \link[PhiCor]{CorIndex.TargetVar} calculates the additional input specific to a habitat.
#' \item \link[PhiCor]{CorIndex.groupEqual} calculates the group-equalised phi for a habitat.
#' \item \link[PhiCor]{CorIndex.nonEqual} calculates the non group-equalised phi for a habitat.
#' \item \link[PhiCor]{CorIndex.xPerm} returns the null phi distribution for a habitat.
#' \item \link[PhiCor]{CorIndex.all} returns the non and group-equalised phi values for all habitats
#' without p-values and is therefore quicker.
#' }
#' Weighted habitat indicator values (IndVal) can also be calculated, but these have not been tested in
#' the way that the phi coefficent has been.
#' The main function for calculating IndVal is \link[PhiCor]{Indicator.all.plusP}
#' These functions are useful in testing or calculating only part
#' of the full outputs from \link[PhiCor]{Indicator.all.plusP}:
#' \itemize{
#' \item \link[PhiCor]{Indicator.nonEqual} Calculated the non group-equalised habitat indicator value (IndVal).
#' \item \link[PhiCor]{Indicator.groupEqual} Calculated the group-equalised species indicator value (IndVal).
#' \item \link[PhiCor]{Indicator.xPerm} Permutates and calculates for each permutation both the non and
#' group-equalised species indicator value (IndVal).
#' \item
#' }
#'
#' The functions \link[PhiCor]{lookup} and \link[PhiCor]{FilePathInTree}
#' might be used elsewhere.
#' @author Jordan Chetcuti
#' @references
#' De Cáceres, M., & Legendre, P. (2009). Associations between species and groups of sites: indices and statistical inference. Ecology, 90(12), 3566–3574.
#' @references
#' 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).
#' @keywords internal
#'
"_PACKAGE"
#> [1] "_PACKAGE"
#' Species 1 data of presence absence and weighting for LCM2015 land cover
#'
#' The data is anonymised data created using LCM2015 \cite{Rowland et al., (2017)} and \cite{NBN Atlas website (2018)}
#'
#' @format A data frame with 1,654 rows and 5 variables:
#' @usage The data is useful in examples of how to use the functions.
#' \describe{
#' \item{LocationID}{A anonymised location}
#' \item{bhab}{LCM2015 land cover description}
#' \item{Proportion}{The proportion of a location that is each habitat}
#' \item{HabId}{An integer ID for each habitat in LCM2015}
#' \item{Species1}{Binary presence absence for "species1"}
#' }
#' @references Rowland, C. S., Morton, R. D., Carrasco, L., McShane, G., O’Neil, A. W., & Wood, C. M. (2017). Land Cover Map 2015 (vector, GB). NERC Environmental Information Data Centre. NERC Environmental Information Data Centre. doi:/10.5285/6c6c9203-7333-4d96-88ab-78925e7a4e73
#' @references NBN Atlas website. (2018). at https://nbnatlas.org. Accessed 13 April 2018.
"Species1"
#' Species 2 data of presence absence and weighting for LCM2015 land cover
#'
#' The data is anonymised data created using LCM2015 \cite{Rowland et al., (2017)} and \cite{NBN Atlas website (2018)}
#'
#' @format A data frame with 1,005 rows and 5 variables:
#' @usage The data is useful in examples of how to use the functions.
#' \describe{
#' \item{LocationID}{A anonymised location}
#' \item{bhab}{LCM2015 land cover description}
#' \item{Proportion}{The proportion of a location that is each habitat}
#' \item{HabId}{An integer ID for each habitat in LCM2015}
#' \item{Species2}{Binary presence absence for "species2"}
#' }
#' @references Rowland, C. S., Morton, R. D., Carrasco, L., McShane, G., O’Neil, A. W., & Wood, C. M. (2017). Land Cover Map 2015 (vector, GB). NERC Environmental Information Data Centre. NERC Environmental Information Data Centre. doi:/10.5285/6c6c9203-7333-4d96-88ab-78925e7a4e73
#' @references NBN Atlas website. (2018). at https://nbnatlas.org. Accessed 13 April 2018.
"Species2"
OutputClass = c(result = data.frame, InVariables = class)
class(OutputClass) = "Output"
print.Output = function(Out){
print(Out$result)
}
default.Output = function(Out){
return(Out$result)
}
#' Lookup function for looking up values from one dataframe for another
#'
#' Works very similarly to vlookup in excel.
#' The output is a field in the dataset you are wanting to copy information into.
#' The syntax is something like:
#' MyDataframe$CopiedField = lookup(MyDataframe$joinField, OtherDataframe$joinField, OutherDataframe$DataToCopy)
#'
#' @param MasterDataField vector - a field with common values to those in another dataset you want to
#' lookup data in.
#' @param AdditionField vector - a field with common values to MasterDataField
#' @param AdditonInputField vector - the data you are importing to the Master dataframe where the
#' AdditionField matches the MasterDataField
#' @return vector - will reurn the vector corrosponding to the MasterDataField that
#' are found in the lookup field AdditonInputField
#' @examples
#' #Lookup DF
#' HabID = c(1, 2, 3, 4, 5, 6)
#' Habitat = c("BLeave", "AGrass", "CGrass", "Water", "Bog", "Heath")
#' Desc = c("Broadleaved and mixed woodland", "Acid grassland","Calcareous grassland", "Inland freshwater", "Sphagnum bog", "Caluna heathland")
#' OpenClosed = c("Closed", "Open", "Open", "Open","Open","Open")
#' DomSpecies = c("Oak", "Bent grass", "Festuca ovina", "NA", "Sphagnum Spp.", "Caluna Spp.")
#' lookupdf = data.frame(HabID, Habitat, Desc, OpenClosed, DomSpecies)
#'
#' #Feild sites dataframe.
#' SiteID = c(1,2,3,4,5,6,7,8,9,10)
#' SiteHabitat = c("BLeave", "AGrass", "BLeave", "AGrass", "Heath", "Bog", "Water", "BLeave", "Water", "Heath")
#' SitesDF = data.frame(SiteID, SiteHabitat)
#'
#' #Looking up the descriptions of the habitats.
#' SitesDF$HabDesc = lookup(SitesDF$SiteHabitat, lookupdf$Habitat, lookupdf$Desc)
#'
#' #You could lookup other information from the lookup table.
#' @export
lookup = function(MasterDataField, AdditionField, AdditionInputField){
index <- match(MasterDataField, AdditionField)
#print(index)
Field = list()
count = 1
for(i in index){
if(!is.na(i)){
Field[count] = as.character(AdditionInputField[i])
}else{
Field[count] = NA
}
count = count + 1
}
return(as.character(Field))
}
#' CorIndex is basic initiation of variables that are the same for
#' all habitats in an analysis of multiple habitats in \link[PhiCor]{PhiCor}.
#'
#' This function is usually called by other function, but could be run
#' seperatly to give many of the variables which go into the equations
#' to calculate the phi coefficent. This is useful if manually checking
#' the calculation.
#'
#' @param InDataframe dataframe - containing the data on species presence absence,
#' habitat and weighting. It also optionally could contain location ID's.
#' @param speciesbinary string - the name of the binary field within InDataframe
#' that contains binary presence absence for a species at the location.
#' @param weighted string - name of habitat weighting field. For example, proportion
#' of habitat, but any weighting with all habitats at a location adding to 1.
#' @param group string - ID of habitats or groups of interest.
#' @param SquareID string - optional ID of each location. Useful later for
#' permutating the locations to calculate p-values.
#' @return 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 \cite{De Cáceres and Legendre (2009)}
#' This is reused to give all the results later.
#' @examples
#' #Using the included dataframe Species1
#' Inputs_species1 = CorIndex(InDataframe = Species1, speciesbinary = 'Species1', weighted = 'Proportion', group = 'HabId', 'LocationID')
#' Inputs_species1$nkoverNk
#'
#' #Using the included dataframe Species2
#' #If you wanted the analysis to be non-weighted.
#' Species2Unweight = Species2[which(Species2$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)
#'
#' @author Jordan Chetcuti
#' @references
#' De Cáceres, M., & Legendre, P. (2009). Associations between species and groups of sites: indices and statistical inference. Ecology, 90(12), 3566–3574.
#' @references
#' 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).
#' @export
CorIndex <- function(InDataframe, speciesbinary,weighted, group, SquareID = NULL){
speciesbinary = InDataframe[,speciesbinary]
weighted = InDataframe[,weighted]
group = InDataframe[,group]
#NextMethod("CorIndex")
CorIndex.default(InDataframe, speciesbinary,weighted, group, SquareID)
}
#' The default method of \link[PhiCor]{CorIndex}.
#'
#' This should not be used independently. See \link[PhiCor]{CorIndex} for usage.
#' @usage NULL
#' @export
CorIndex.default <- function(InDataframe, speciesbinary,weighted, group, SquareID = NULL){
rtrn <- list()
class(rtrn) <- "CorIndexVar"
### Weighting the variables Np and np ( calculated for every habitat as Nk and nk and then
###later individually selected as Np and np)
Nk = aggregate(weighted ~ group, InDataframe, sum)
Nk = setNames(Nk, c("group", "Nk"))
nk = aggregate(speciesbinary * weighted ~ group, InDataframe, sum)
nk = setNames(nk, c("group", "nk"))
rtrn$Nk = Nk
rtrn$nk = nk
GroupDF = Nk
GroupDF[,2] =NULL
rtrn$GroupDF = GroupDF
N = sum(weighted)
rtrn$N = N
K = length(Nk$group)
rtrn$K = K
Ngp = N/K
rtrn$Ngp = Ngp
nkoverNk = nk/Nk
nkoverNk = setNames(nkoverNk, c("group", "nkoverNk"))
rtrn$nkoverNk = nkoverNk
sumnkoverNk = sum(nkoverNk$nkoverNk)
rtrn$sumnkoverNk = sumnkoverNk
ng = Ngp*sumnkoverNk
rtrn$ng = ng
n = sum(nk$nk)
rtrn$n = n
return(rtrn)
}
#' CorIndex.TargetVar is creates the variables that are particular to a
#' habitat that were not created in the \link[PhiCor]{CorIndex}
#'
#' This function is usually called by other function, but could be run
#' seperatly to give many of the variables which go into the equations
#' to calculate the phi coefficent. This is useful if manually checking
#' the calculation.
#'
#' @param CorIndexVarInput is the custom class "CorIndexVar" output from \link[PhiCor]{CorIndex}
#' @param targetgroup Integer - ID of the habitat of interest.
#' @return a custom class "CorIndexVar" containing all values which are the
#' same for all habitats and don't need to be recalculated for every
#' habitat and the ones for the specific target habitat. Including those from
#' CorIndex Nk, nk, the habitats "GroupDF", N, K, Ngp, nkoverNk ng & n and
#' aditionally np, Np & ngp See \cite{De Cáceres and Legendre (2009)}
#' This is reused to give all the results later.
#'
#' @examples
#' #Builing on the examples from \link[PhiCor]{CorIndex}
#' #Using the included dataframe Species1
#' Inputs_species1 = CorIndex(InDataframe = Species1, speciesbinary = 'Species1', weighted = 'Proportion', group = 'HabId', 'LocationID')
#' Inputs_species1$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)
#'
#'
#' #Using the included dataframe Species2
#' #If you wanted the analysis to be non-weighted.
#' Species2Unweight = Species2[which(Species2$Proportion==1),]
#' #This analysis is then the unweighted version of the analysis. SquareID is not needed.
#' Inputs_species2 = CorIndex(InDataframe = Species2Unweight, speciesbinary = 'Species2', weighted = 'Proportion', group = 'HabId')
#' names(Inputs_species2)
#'
#' Looking at habitat 4
#' Inputs_species2_hab4 = CorIndex.TargetVar(Inputs_species2, 4)
#'
#'
#' @seealso \link[PhiCor]{CorIndex}
#' @author Jordan Chetcuti
#' @references
#' De Cáceres, M., & Legendre, P. (2009). Associations between species and groups of sites: indices and statistical inference. Ecology, 90(12), 3566–3574.
#' @references
#' Chetcuti, J., Kunin, W. E. & Bullock, J. M. A weighting method to improve habitat association analysis: tested on British carabids. Ecography (Cop.). 42, 1395–1404 (2019).
#' @export
CorIndex.TargetVar <- function(CorIndexVarInput, targetgroup){
rtrn <- list()
class(rtrn) <- "CorIndexVar"
rtrn = CorIndexVarInput
nk = CorIndexVarInput$nk
Nk = CorIndexVarInput$Nk
Ngp = CorIndexVarInput$Ngp
np = nk$nk[nk$group==targetgroup]
Np = Nk$Nk[Nk$group==targetgroup]
rtrn$np = np
rtrn$Np = Np
ngp = Ngp*np/Np
rtrn$ngp = ngp
return(rtrn)
}
#' Calculated the group-equalised Phi Coefficient of correlation.
#'
#' This function calculates the group-equalised version of the Phi coefficient of
#' correlation \cite{Tichy and Chytry (2006), De Cáceres and Legendre (2009)}. This is
#' useful alone to calculate the group-equalised version for a single habitat of interest
#' among multiple other habitats.
#'
#' @param CorIndexTargetVarInput a custom class "CorIndexVar" containing all values from
#' from both \link[PhiCor]{CorIndex} and \link[PhiCor]{CorIndex.TargetVar}
#' @return Phi - a floating point number between -1. and 1.
#' @examples
#' #Builing on the examples from \link[PhiCor]{CorIndex.TargetVar}
#' #Using the included dataframe Species1
#' Inputs_species1 = CorIndex(InDataframe = Species1, speciesbinary = 'Species1', weighted = 'Proportion', group = 'HabId', 'LocationID')
#' Inputs_species1$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$Proportion==1),]
#' #This analysis is then the unweighted version of the analysis. SquareID is not needed.
#' Inputs_species2 = CorIndex(InDataframe = Species2Unweight, speciesbinary = 'Species2', weighted = 'Proportion', group = 'HabId')
#' names(Inputs_species2)
#'
#' #Looking at habitat 4
#' Inputs_species2_hab4 = CorIndex.TargetVar(Inputs_species2, 4)
#'
#' species2_hab4_GE_Phi = CorIndex.groupEqual(Inputs_species2_hab4)
#'
#'
#' @seealso \link[PhiCor]{PhiCor}
#' @author Jordan Chetcuti
#' @references
#' Tichy, L., & Chytry, M. (2006). Statistical determination of diagnostic species for site groups of unequal size. Journal of Vegetation Science, 17(6), 809–818.
#' @references
#' De Cáceres, M., & Legendre, P. (2009). Associations between species and groups of sites: indices and statistical inference. Ecology, 90(12), 3566–3574.
#' @references
#' Chetcuti, J., Kunin, W. E. & Bullock, J. M. A weighting method to improve habitat association analysis: tested on British carabids. Ecography (Cop.). 42, 1395–1404 (2019).
#' @export
CorIndex.groupEqual <- function(CorIndexTargetVarInput){
N = CorIndexTargetVarInput$N
ngp = CorIndexTargetVarInput$ngp
ng = CorIndexTargetVarInput$ng
Ngp = CorIndexTargetVarInput$Ngp
Requ = (N*ngp - ng*Ngp)/sqrt((N*ng - ng^2)*(N*Ngp - Ngp^2))
return(Requ)
}
#' Calculated the non group-equalised Phi Coefficient of correlation.
#'
#' This function calculates the non group-equalised version of the Phi coefficient of
#' correlation \cite{De Cáceres and Legendre (2009)}. This is
#' useful alone to calculate the non group-equalised version for a single habitat of interest
#' among multiple other habitats.
#'
#' @param CorIndexTargetVarInput a custom class "CorIndexVar" containing all values from
#' from both \link[PhiCor]{CorIndex} and \link[PhiCor]{CorIndex.TargetVar}
#' @return Phi - a floating point number between -1. and 1.
#' @examples
#' #Builing on the examples from \link[PhiCor]{CorIndex.TargetVar}
#' #Using the included dataframe Species1
#' Inputs_species1 = CorIndex(InDataframe = Species1, speciesbinary = 'Species1', weighted = 'Proportion', group = 'HabId', 'LocationID')
#' Inputs_species1$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_Phi = CorIndex.nonEqual(Inputs_species1_hab1)
#'
#'
#'
#' #Using the included dataframe Species2
#' #If you wanted the analysis to be non-weighted.
#' Species2Unweight = Species2[which(Species2$Proportion==1),]
#' #This analysis is then the unweighted version of the analysis. SquareID is not needed.
#' Inputs_species2 = CorIndex(InDataframe = Species2Unweight, speciesbinary = 'Species2', weighted = 'Proportion', group = 'HabId')
#' names(Inputs_species2)
#'
#' #Looking at habitat 4
#' Inputs_species2_hab4 = CorIndex.TargetVar(Inputs_species2, 4)
#'
#' species2_hab4_Phi = CorIndex.nonEqual(Inputs_species2_hab4)
#' @seealso \link[PhiCor]{PhiCor}
#' @author Jordan Chetcuti
#' Tichy, L., & Chytry, M. (2006). Statistical determination of diagnostic species for site groups of unequal size. Journal of Vegetation Science, 17(6), 809–818.
#' @references
#' De Cáceres, M., & Legendre, P. (2009). Associations between species and groups of sites: indices and statistical inference. Ecology, 90(12), 3566–3574.
#' @references
#' Chetcuti, J., Kunin, W. E. & Bullock, J. M. A weighting method to improve habitat association analysis: tested on British carabids. Ecography (Cop.). 42, 1395–1404 (2019).
#' @export
CorIndex.nonEqual <- function(CorIndexTargetVarInput){
N = CorIndexTargetVarInput$N
np = CorIndexTargetVarInput$np
n = CorIndexTargetVarInput$n
Np = CorIndexTargetVarInput$Np
Rnon = (N*np - n*Np)/sqrt((N*n - n^2)*(N*Np - Np^2))
return(Rnon)
}
#' Calculated both the non and group-equalised Phi Coefficient of correlation for all habitats.
#'
#' This function calculates the non and group-equalised versions of the Phi coefficient of
#' correlation \cite{Tichy and Chytry (2006), De Cáceres and Legendre (2009)}.
#'
#' It doesn't calculate any p-values by permutation and therefore calculates very quickly
#' (unlike the permutated version). It is therefore a useful first step and/or useful
#' when the p-values aren't important.
#'
#' @param CorIndexVarInput a custom class "CorIndexVar" containing all values from
#' from only \link[PhiCor]{CorIndex}. It calls \link[PhiCor]{CorIndex.TargetVar} within
#' the function.
#' @return dataframe of habitat ID and non equalised (R) and equalised (Rg) Phi values
#' the R and Rg numbers are both floating point number between -1. and 1.
#' @examples
#' #Builing on the examples from \link[PhiCor]{CorIndex}
#'
#' #Using the included dataframe Species1
#' Inputs_species1 = CorIndex(InDataframe = Species1, speciesbinary = 'Species1', weighted = 'Proportion', group = 'HabId', 'LocationID')
#' Inputs_species1$nkoverNk
#' Species1_AllPhi = CorIndex.all(Inputs_species1)
#'
#'
#'
#' #Using the included dataframe Species2
#' #If you wanted the analysis to be non-weighted.
#' Species2Unweight = Species2[which(Species2$Proportion==1),]
#' #This analysis is then the unweighted version of the analysis. SquareID is not needed.
#' Inputs_species2 = CorIndex(InDataframe = Species2Unweight, speciesbinary = 'Species2', weighted = 'Proportion', group = 'HabId')
#' names(Inputs_species2)
#' Species2_AllPhi = CorIndex.all(Inputs_species2)
#'
#'
#' @seealso \link[PhiCor]{PhiCor}
#' @author Jordan Chetcuti
#' @references
#' Tichy, L., & Chytry, M. (2006). Statistical determination of diagnostic species for site groups of unequal size. Journal of Vegetation Science, 17(6), 809–818.
#' @references
#' De Cáceres, M., & Legendre, P. (2009). Associations between species and groups of sites: indices and statistical inference. Ecology, 90(12), 3566–3574.
#' @references
#' Chetcuti, J., Kunin, W. E. & Bullock, J. M. A weighting method to improve habitat association analysis: tested on British carabids. Ecography (Cop.). 42, 1395–1404 (2019).
#' @export
CorIndex.all <- function(CorIndexVarInput){
OutDF = CorIndexVarInput$GroupDF
OutDF$R = 0.0000
OutDF$Rg = 0.0000
for(i in 1:nrow(OutDF)) {
targetVariables = CorIndex.TargetVar(CorIndexVarInput,OutDF[i,1])
print(OutDF[i,1])
OutDF[i,2]= CorIndex.nonEqual(targetVariables)
OutDF[i,3]= CorIndex.groupEqual(targetVariables)
}
return(OutDF)
}
CorIndex.APerm <- function(InDataframe, FieldToPerm, SquareID = NULL){
copyDF = InDataframe
if(is.null(SquareID)){
copyDF[,FieldToPerm] = sample(copyDF[,FieldToPerm], nrow(copyDF), replace = FALSE, prob = NULL)
}else{
AggCopyDF = aggregate(InDataframe[,FieldToPerm] ~ InDataframe[,SquareID], InDataframe, min)
AggCopyDF = setNames(AggCopyDF, c(SquareID, FieldToPerm))
AggCopyDF[,FieldToPerm] = sample(AggCopyDF[,FieldToPerm], nrow(AggCopyDF), replace = FALSE, prob = NULL)
copyDF[,FieldToPerm] = as.numeric(lookup(copyDF[,SquareID], AggCopyDF[,SquareID], AggCopyDF[,2]))
}
return(copyDF)
}
CorIndex.toroidalAPerm <- function(InDataframe, FieldToPerm, SquareID = NULL){
copyDF = InDataframe
if(is.null(SquareID)){
rows = nrow(copyDF)
MoveOn = sample(0:rows, 1)
copyDF$Index = row.names(copyDF)
copyDF$Index = as.numeric(copyDF$Index) + MoveOn
copyDF$Index = ifelse(copyDF$Index>rows,copyDF$Index - rows, copyDF$Index )
temp = copyDF[,c(FieldToPerm, "Index")]
temp = temp[order(temp$Index),]
copyDF[,FieldToPerm] = temp[,FieldToPerm]
copyDF$Index = NULL
#copyDF[,FieldToPerm] = sample(copyDF[,FieldToPerm], nrow(copyDF), replace = FALSE, prob = NULL)
}else{
AggCopyDF = aggregate(InDataframe[,FieldToPerm] ~ InDataframe[,SquareID], InDataframe, min)
AggCopyDF = setNames(AggCopyDF, c(SquareID, FieldToPerm))
rows = nrow(AggCopyDF)
MoveOn = sample(0:rows, 1)
AggCopyDF$Index = row.names(AggCopyDF)
AggCopyDF$Index = as.numeric(AggCopyDF$Index) + MoveOn
AggCopyDF$Index = ifelse(AggCopyDF$Index>rows,AggCopyDF$Index - rows, AggCopyDF$Index )
temp = AggCopyDF[,c(FieldToPerm, "Index")]
temp = temp[order(temp$Index),]
AggCopyDF[,FieldToPerm] = temp[,FieldToPerm]
AggCopyDF$Index = NULL
AggCopyDF[,FieldToPerm] = sample(AggCopyDF[,FieldToPerm], nrow(AggCopyDF), replace = FALSE, prob = NULL)
copyDF[,FieldToPerm] = as.numeric(lookup(copyDF[,SquareID], AggCopyDF[,SquareID], AggCopyDF[,2]))
}
return(copyDF)
}
#' Permutates and calculates for each permutation both the non and group-equalised
#' Phi coefficent.
#'
#' This function purmutaes and calculates the non and group-equalised versions of the Phi coefficient of
#' correlation \cite{Tichy and Chytry (2006), De Cáceres and Legendre (2009)}.
#' It is useful as it gives the distribution of phi values if the species are distributed
#' randomly across the locations, the phi value for the actual data is compared to this
#' distribution to calculate the p-value. The distribution is a useful output to visualise
#' the distribution.
#'
#' @param InDataframe dataframe - containing the data on species presence absence,
#' habitat and weighting. It also optionally could contain location ID's.
#' @param FieldToPerm string - the name of the binary field within InDataframe
#' that contains binary presence absence for a species at the location.
#' @param weighted string - name of habitat weighting field. For example, proportion
#' of habitat, but any weighting with all habitats at a location adding to 1.
#' @param groupsField string - ID of habitats or groups of interest.
#' @param targetgroup Integer - ID of the habitat of interest.
#' @param x Integer - optional - the number of permutation, set as 1000 by default.
#' @param 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.
#' @param toroidal Boolean - If you suspect that your data is spatially auto-correlated you should use
#' a toroidal permutation.
#' @return Two data frames, one for the group-equalised $`GroupEqualPermList` and one
#' for the non group-equalised $NonGroupEqualPermList. It also returns the number of
#' permutations $Permutations.
#' @examples
#' #Builing on the examples from \link[PhiCor]{CorIndex.nonEqual}
#' #Using the included dataframe Species1
#' Inputs_species1 = CorIndex(InDataframe = Species1, speciesbinary = 'Species1', weighted = 'Proportion', group = 'HabId', 'LocationID')
#' Inputs_species1$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_Phi = CorIndex.nonEqual(Inputs_species1_hab1)
#'
#' #histogram showing the output of CorIndex.xPerm for habitat 1 in comparison to the phi value from actual data.
#' hist(Hab1_PermutatedPhi$NonGroupEqualPermList, main = "Non group-equalised distribution", xlab = "Phi values", breaks = 100)
#' abline(v=species1_hab1_Phi, col="red")
#' text((species1_hab1_Phi + 0.006), 15, "Calulated phi value", col = "red", srt=90)
#'
#'
#'
#' @seealso \link[PhiCor]{PhiCor}
#' @author Jordan Chetcuti
#' @export
CorIndex.xPerm <- function(InDataframe, FieldToPerm, weighted, groupsField, targetGroup, x = 1000, SquareID = NULL, toroidal = FALSE){
GroupEqualPermList = c()
NonGroupEqualPermList = c()
for(i in 1:x){
if (toroidal){
Perm1 = CorIndex.toroidalAPerm(InDataframe, FieldToPerm, SquareID)
}else{
Perm1 = CorIndex.APerm(InDataframe, FieldToPerm, SquareID)
}
UnchangePerm = CorIndex(Perm1, FieldToPerm, weighted, groupsField)
VarPerm1 = CorIndex.TargetVar(UnchangePerm, targetGroup)
GroupEqualPermList = c(GroupEqualPermList, CorIndex.groupEqual(VarPerm1))
NonGroupEqualPermList = c(NonGroupEqualPermList, CorIndex.nonEqual(VarPerm1))
}
out = list("GroupEqualPermList" = GroupEqualPermList, "NonGroupEqualPermList" = NonGroupEqualPermList, "Permutations" = x)
return(out)
}
#' Calculated both the non and group-equalised Phi Coefficient of correlation for all habitats
#' and the p-values.
#'
#' This function calculates the non and group-equalised versions of the Phi coefficient of
#' correlation \cite{Tichy and Chytry (2006), De Cáceres and Legendre (2009)}.
#'
#' 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.
#'
#' @param InDataframe dataframe - containing the data on species presence absence,
#' habitat and weighting. It also optionally could contain location ID's.
#' @param speciesbinary string - the name of the binary field within InDataframe
#' that contains binary presence absence for a species at the location.
#' @param weighted string - name of habitat weighting field. For example, proportion
#' of habitat, but any weighting with all habitats at a location adding to 1.
#' @param group string - ID of habitats or groups of interest.
#' @param numberIteration Integer - optional - the number of permutation, set as 1000 by default.
#' @param 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.
#' @param toroidal Boolean - If you suspect that your data is spatially auto-correlated you should use
#' a toroidal permutation.
#' @return 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 \cite{De Cáceres and Legendre (2009)}. To this is added another custom
#' class "Output" as $`result` which gives the habitat ID, R (non group-equalised phi)
#' pR (the p-value for R), Rg (group-equalised phi) and pRg (the p-value for Rg). This
#' "Output" class is the main result, the CorIndexVar is included as metadata of the
#' input values.
#' @examples
#' #Using the included dataframe Species1
#' Species1_AllPhiPvalues = CorIndex.all.plusP(InDataframe = Species1, speciesbinary = "Species1", weighted = 'Proportion', group = 'HabId', SquareID = 'LocationID')
#' names(Species1_AllPhiPvalues)
#' Species1_AllPhiPvalues$`result`
#' @seealso \link[PhiCor]{PhiCor}
#' @author Jordan Chetcuti
#' @references
#' Tichy, L., & Chytry, M. (2006). Statistical determination of diagnostic species for site groups of unequal size. Journal of Vegetation Science, 17(6), 809–818.
#' @references
#' De Cáceres, M., & Legendre, P. (2009). Associations between species and groups of sites: indices and statistical inference. Ecology, 90(12), 3566–3574.
#' @references
#' 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).
#' @export
CorIndex.all.plusP <- function(InDataframe, speciesbinary, weighted, group, numberIteration = 1000, SquareID = NULL, toroidal = FALSE){
CorIndexVarInput = CorIndex(InDataframe, speciesbinary, weighted, group, SquareID)
OutDF = CorIndexVarInput$GroupDF
OutDF$R = 0.0000000000
OutDF$pR = 0.0000000000
OutDF$Rg = 0.0000000000
OutDF$pRG = 0.0000000000
for(i in 1:nrow(OutDF)) {
targetVariables = CorIndex.TargetVar(CorIndexVarInput,OutDF[i,1])
print(OutDF[i,1])
Distributions = CorIndex.xPerm(InDataframe, speciesbinary,weighted,group,OutDF[i,"group"], numberIteration, SquareID, toroidal)
OutDF[i,2]= CorIndex.nonEqual(targetVariables)
#Calc each of the probabilities.
length(Distributions$NonGroupEqualPermList[Distributions$NonGroupEqualPermList >= OutDF[i,2]])/numberIteration
length(Distributions$NonGroupEqualPermList[Distributions$NonGroupEqualPermList <= OutDF[i,2]])/numberIteration
OutDF[i,3] = min(length(Distributions$NonGroupEqualPermList[Distributions$NonGroupEqualPermList >= OutDF[i,2]])/numberIteration , length(Distributions$NonGroupEqualPermList[Distributions$NonGroupEqualPermList <= OutDF[i,2]])/numberIteration)
OutDF[i,4]= CorIndex.groupEqual(targetVariables)
OutDF[i,5] = min(length(Distributions$GroupEqualPermList[Distributions$GroupEqualPermList >= OutDF[i,4]])/numberIteration , length(Distributions$GroupEqualPermList[Distributions$GroupEqualPermList <= OutDF[i,4]])/numberIteration)
OutputClass$result = OutDF
OutputClass$InVariables = CorIndexVarInput
}
return(OutputClass)
}
#' Calculated the non group-equalised habitat indicator value (IndVal).
#'
#' This function calculates the non group-equalised version of the habitat indicator value (IndVal)
#' \cite{De Cáceres and Legendre (2009)}. This is
#' useful alone to calculate the non group-equalised version for a single habitat of interest
#' among multiple other habitats.
#'
#' @param CorIndexTargetVarInput a custom class "CorIndexVar" containing all values from
#' from both \link[PhiCor]{CorIndex} and \link[PhiCor]{CorIndex.TargetVar}
#' @return IndVal - a floating point number between 0 and 1.
#' @examples
#' #Builing on the examples from \link[PhiCor]{CorIndex.TargetVar}
#' #Using the included dataframe Species1
#' Inputs_species1 = CorIndex(InDataframe = Species1, speciesbinary = 'Species1', weighted = 'Proportion', group = 'HabId', 'LocationID')
#' Inputs_species1$nkoverNk
#' #Using CorIndex.TargetVar, the input is the output of Indicator. In this case we are interested in
#' #habitat 1 (using the ID for the habitat)
#' Inputs_species1_hab1 = Indicator.TargetVar(Inputs_species1, 1)
#'
#' species1_hab1_Phi = Indicator.nonEqual(Inputs_species1_hab1)
#'
#'
#'
#' #Using the included dataframe Species2
#' #If you wanted the analysis to be non-weighted.
#' Species2Unweight = Species2[which(Species2$Proportion==1),]
#' #This analysis is then the unweighted version of the analysis. SquareID is not needed.
#' Inputs_species2 = CorIndex(InDataframe = Species2Unweight, speciesbinary = 'Species2', weighted = 'Proportion', group = 'HabId')
#' names(Inputs_species2)
#'
#' #Looking at habitat 4
#' Inputs_species2_hab4 = Indicator.TargetVar(Inputs_species2, 4)
#'
#' species2_hab4_IndVal = Indicator.nonEqual(Inputs_species2_hab4)
#' @seealso \link[PhiCor]{PhiCor}
#' @author Jordan Chetcuti
#' @references
#' De Cáceres, M., & Legendre, P. (2009). Associations between species and groups of sites: indices and statistical inference. Ecology, 90(12), 3566–3574.
#' @references
#' Chetcuti, J., Kunin, W. E. & Bullock, J. M. A weighting method to improve habitat association analysis: tested on British carabids. Ecography (Cop.). 42, 1395–1404 (2019).
#' @export
Indicator.nonEqual <- function(CorIndexTargetVarInput){
N = CorIndexTargetVarInput$N
np = CorIndexTargetVarInput$np
n = CorIndexTargetVarInput$n
Np = CorIndexTargetVarInput$Np
########
Rnon = sqrt((np/n)*(np/Np))
return(Rnon)
}
#' Calculated the group-equalised species indicator value (IndVal).
#'
#' This function calculates the group-equalised version of the species indicator value (IndVal)
#' \cite{De Cáceres and Legendre (2009)}. This is
#' useful alone to calculate the group-equalised version for a single habitat of interest
#' among multiple other habitats.
#'
#' @param CorIndexTargetVarInput a custom class "CorIndexVar" containing all values from
#' from both \link[PhiCor]{CorIndex} and \link[PhiCor]{CorIndex.TargetVar}
#' @return IndVal - a floating point number between 0 and 1.
#' @examples
#' #Builing on the examples from \link[PhiCor]{CorIndex.TargetVar}
#' #Using the included dataframe Species1
#' Inputs_species1 = CorIndex(InDataframe = Species1, speciesbinary = 'Species1', weighted = 'Proportion', group = 'HabId', 'LocationID')
#' Inputs_species1$nkoverNk
#' #Using CorIndex.TargetVar, the input is the output of Indicator. 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_IndVal = Indicator.groupEqual(Inputs_species1_hab1)
#'
#'
#'
#' #Using the included dataframe Species2
#' #If you wanted the analysis to be non-weighted.
#' Species2Unweight = Species2[which(Species2$Proportion==1),]
#' #This analysis is then the unweighted version of the analysis. SquareID is not needed.
#' Inputs_species2 = CorIndex(InDataframe = Species2Unweight, speciesbinary = 'Species2', weighted = 'Proportion', group = 'HabId')
#' names(Inputs_species2)
#'
#' #Looking at habitat 4
#' Inputs_species2_hab4 = Indicator.TargetVar(Inputs_species2, 4)
#'
#' species2_hab4_GE_IndVal = Indicator.groupEqual(Inputs_species2_hab4)
#'
#'
#' @seealso \link[PhiCor]{PhiCor}
#' @author Jordan Chetcuti
#' @references
#' De Cáceres, M., & Legendre, P. (2009). Associations between species and groups of sites: indices and statistical inference. Ecology, 90(12), 3566–3574.
#' @references
#' Chetcuti, J., Kunin, W. E. & Bullock, J. M. A weighting method to improve habitat association analysis: tested on British carabids. Ecography (Cop.). 42, 1395–1404 (2019).
#' @export
Indicator.groupEqual <- function(CorIndexTargetVarInput){
N = CorIndexTargetVarInput$N
ngp = CorIndexTargetVarInput$ngp
ng = CorIndexTargetVarInput$ng
Ngp = CorIndexTargetVarInput$Ngp
SumNks = sum(CorIndexTargetVarInput$nk/CorIndexTargetVarInput$Nk)
################
Requ = sqrt(((ngp/Ngp)/SumNks)*(ngp/Ngp))
return(Requ)
}
#' Permutates and calculates for each permutation both the non and group-equalised
#' species indicator value (IndVal).
#'
#' This function purmutaes and calculates the non and group-equalised versions of species indicator
#' value (IndVal) \cite{De Cáceres and Legendre (2009)}.
#' It is useful as it gives the distribution of IndVal values if the species are distributed
#' randomly across the locations, the IndVal value for the actual data is compared to this
#' distribution to calculate the p-value. The distribution is a useful output to visualise
#' the distribution.
#'
#' @param InDataframe dataframe - containing the data on species presence absence,
#' habitat and weighting. It also optionally could contain location ID's.
#' @param FieldToPerm string - the name of the binary field within InDataframe
#' that contains binary presence absence for a species at the location.
#' @param weighted string - name of habitat weighting field. For example, proportion
#' of habitat, but any weighting with all habitats at a location adding to 1.
#' @param groupsField string - ID of habitats or groups of interest.
#' @param targetgroup Integer - ID of the habitat of interest.
#' @param x Integer - optional - the number of permutation, set as 1000 by default.
#' @param 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.
#' @param toroidal Boolean - If you suspect that your data is spatially auto-correlated you should use
#' a toroidal permutation.
#' @return Two data frames, one for the group-equalised $`GroupEqualPermList` and one
#' for the non group-equalised $NonGroupEqualPermList. It also returns the number of
#' permutations $Permutations.
#' @examples
#' #Building on the examples from \link[PhiCor]{Indicator.nonEqual}
#' #Using the included dataframe Species1
#' Inputs_species1 = CorIndex(InDataframe = Species1, speciesbinary = 'Species1', weighted = 'Proportion', group = 'HabId', 'LocationID')
#' Inputs_species1$nkoverNk
#' #Using CorIndex.TargetVar, the input is the output of Indicator. 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_Indval = Indicator.nonEqual(Inputs_species1_hab1)
#'
#' #histogram showing the output of Indicator.xPerm for habitat 1 in comparison to the IndVal value from actual data.
#' hist(Hab1_PermutatedPhi$NonGroupEqualPermList, main = "Non group-equalised distribution", xlab = "IndVal values", breaks = 100)
#' abline(v=species1_hab1_IndVal, col="red")
#' text((species1_hab1_IndVal + 0.006), 15, "Calulated IndVal value", col = "red", srt=90)
#'
#'
#'
#' @seealso \link[PhiCor]{PhiCor}
#' @author Jordan Chetcuti
#' @export
Indicator.xPerm <- function(InDataframe, FieldToPerm, weighted, groupsField, targetGroup, x = 1000, SquareID = NULL, toroidal = FALSE){
GroupEqualPermList = c()
NonGroupEqualPermList = c()
for(i in 1:x){
if (toroidal){
Perm1 = CorIndex.toroidalAPerm(InDataframe, FieldToPerm, SquareID)
}else{
Perm1 = CorIndex.APerm(InDataframe, FieldToPerm, SquareID)
}
UnchangePerm = CorIndex(Perm1, FieldToPerm, weighted, groupsField)
VarPerm1 = CorIndex.TargetVar(UnchangePerm, targetGroup)
#########
GroupEqualPermList = c(GroupEqualPermList, Indicator.groupEqual(VarPerm1))
NonGroupEqualPermList = c(NonGroupEqualPermList, Indicator.nonEqual(VarPerm1))
}
out = list("GroupEqualPermList" = GroupEqualPermList, "NonGroupEqualPermList" = NonGroupEqualPermList, "Permutations" = x)
return(out)
}
#' Calculated both the non and group-equalised IndVal for all habitats
#' and the p-values.
#'
#' This function calculates the non and group-equalised versions of an habitat indicator value (IndVal)
#' correlation \cite{De Cáceres and Legendre (2009)}.
#'
#' It calculate p-values by permutation and therefore calculates take a bit of time.
#'
#' @param InDataframe dataframe - containing the data on species presence absence,
#' habitat and weighting. It also optionally could contain location ID's.
#' @param speciesbinary string - the name of the binary field within InDataframe
#' that contains binary presence absence for a species at the location.
#' @param weighted string - name of habitat weighting field. For example, proportion
#' of habitat, but any weighting with all habitats at a location adding to 1.
#' @param group string - ID of habitats or groups of interest.
#' @param numberIteration Integer - optional - the number of permutation, set as 1000 by default.
#' @param 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.
#' @param toroidal Boolean - If you suspect that your data is spatially auto-correlated you should use
#' a toroidal permutation.
#' @return 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 \cite{De Cáceres and Legendre (2009)}. To this is added another custom
#' class "Output" as $`result` which gives the habitat ID, I (non group-equalised IndVal)
#' pI (the p-value for I), Ig (group-equalised IndVal) and Ig (the p-value for Ig). This
#' "Output" class is the main result, the CorIndexVar is included as metadata of the
#' input values.
#' @examples
#' #Using the included dataframe Species1
#' Species1_AllIndValvalues = Indicator.all.plusP(InDataframe = Species1, speciesbinary = "Species1", weighted = 'Proportion', group = 'HabId', SquareID = 'LocationID')
#' names(Species1_AllIndValvalues)
#' Species1_AllIndValvalues$`result`
#' @seealso \link[PhiCor]{PhiCor}
#' @author Jordan Chetcuti
#' @references
#' De Cáceres, M., & Legendre, P. (2009). Associations between species and groups of sites: indices and statistical inference. Ecology, 90(12), 3566–3574.
#' @references
#' 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).
#' @export
Indicator.all.plusP <- function(InDataframe, speciesbinary, weighted, group, numberIteration = 1000, SquareID = NULL, toroidal = FALSE){
CorIndexVarInput = CorIndex(InDataframe, speciesbinary, weighted, group, SquareID)
OutDF = CorIndexVarInput$GroupDF
OutDF$I = 0.0000000000
OutDF$pI = 0.0000000000
OutDF$Ig = 0.0000000000
OutDF$pIG = 0.0000000000
for(i in 1:nrow(OutDF)) {
targetVariables = CorIndex.TargetVar(CorIndexVarInput,OutDF[i,1])
print(OutDF[i,1])
Distributions = Indicator.xPerm(InDataframe, speciesbinary,weighted,group,i, numberIteration, SquareID, toroidal)
OutDF[i,2]= Indicator.nonEqual(targetVariables)
#Calc each of the probabilities.
length(Distributions$NonGroupEqualPermList[Distributions$NonGroupEqualPermList >= OutDF[i,2]])/numberIteration
length(Distributions$NonGroupEqualPermList[Distributions$NonGroupEqualPermList <= OutDF[i,2]])/numberIteration
OutDF[i,3] = min(length(Distributions$NonGroupEqualPermList[Distributions$NonGroupEqualPermList >= OutDF[i,2]])/numberIteration , length(Distributions$NonGroupEqualPermList[Distributions$NonGroupEqualPermList <= OutDF[i,2]])/numberIteration)
OutDF[i,4]= Indicator.groupEqual(targetVariables)
OutDF[i,5] = min(length(Distributions$GroupEqualPermList[Distributions$GroupEqualPermList >= OutDF[i,4]])/numberIteration , length(Distributions$GroupEqualPermList[Distributions$GroupEqualPermList <= OutDF[i,4]])/numberIteration)
OutputClass$result = OutDF
OutputClass$InVariables = CorIndexVarInput
}
return(OutputClass)
}
#' Looks for a filename in the current folder and then expands up and along the file tree
#' looking for the filename.
#'
#' This isn't really part of calculating the Phi coefficient, but is useful in finding a
#' file which has been reorganised within a project. Imperfect as will return multiple
#' paths if the filename is used twice locally.
#'
#' @param StringFileName string filename
#' @return Filepath including the input StringFileName for all files found in the vicinity.
#' If it finds multiple files it returns a list of paths which can be accessed individually
#' using [1], [2]...etc.
#' @examples
#' #Will return as "character" all R related files in the directory you are currently working on
#' PathRfiles = FilePathInTree(".R")
#' #If there is only one file PathRfiles would return it, but with multiple PathRfiles[1] would return the first or only.
#' PathRfiles[1]
#'
#' @export
FilePathInTree = function(StringFileName){
OldPath = getwd()
while(length(dir(getwd(), StringFileName,recursive=TRUE))==0){
setwd('..')
}
FilePath = file.path(getwd(),dir(getwd(), StringFileName,recursive=TRUE))
setwd(OldPath)
rm(OldPath)
return(FilePath)
}
Zabados/PhiCor documentation built on Oct. 9, 2021, 1:24 a.m.
R Package Documentation
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Defines functions FilePathInTree Indicator.all.plusP Indicator.xPerm Indicator.groupEqual Indicator.nonEqual CorIndex.all.plusP CorIndex.xPerm CorIndex.toroidalAPerm CorIndex.APerm CorIndex.all CorIndex.nonEqual CorIndex.groupEqual CorIndex.TargetVar CorIndex.default CorIndex lookup default.Output print.Output
Documented in CorIndex CorIndex.all CorIndex.all.plusP CorIndex.default CorIndex.groupEqual CorIndex.nonEqual CorIndex.TargetVar CorIndex.xPerm FilePathInTree Indicator.all.plusP Indicator.groupEqual Indicator.nonEqual Indicator.xPerm lookup
#' Package for calculation the Weighted Phi Coefficient of association
#'
#' This package can be used to calculate the weighted phi coeficient of
#' association using a weighting for habitats within a location, or
#' can calculate the unweighted original version from
#' \cite{De Cáceres and Legendre (2009)} by using a weighting of one.
#' The package also allows the group-equalised version to be run.
#'
#' The Phi Coefficent of association gives the habitat preference, or
#' site fidelity of habitat sites.
#'
#' @details
#' The main function in PhiCor is \link[PhiCor]{CorIndex.all.plusP}.
#' This will calculate all phi corelation of association values and
#' purmutate to give p-values.
#'
#' The other Phi coefficent functions are useful in testing or calculating only part
#' of the full outputs from \link[PhiCor]{CorIndex.all.plusP}:
#' \itemize{
#' \item \link[PhiCor]{CorIndex} calculates the inputs that don't change between habitats.
#' \item \link[PhiCor]{CorIndex.TargetVar} calculates the additional input specific to a habitat.
#' \item \link[PhiCor]{CorIndex.groupEqual} calculates the group-equalised phi for a habitat.
#' \item \link[PhiCor]{CorIndex.nonEqual} calculates the non group-equalised phi for a habitat.
#' \item \link[PhiCor]{CorIndex.xPerm} returns the null phi distribution for a habitat.
#' \item \link[PhiCor]{CorIndex.all} returns the non and group-equalised phi values for all habitats
#' without p-values and is therefore quicker.
#' }
#' Weighted habitat indicator values (IndVal) can also be calculated, but these have not been tested in
#' the way that the phi coefficent has been.
#' The main function for calculating IndVal is \link[PhiCor]{Indicator.all.plusP}
#' These functions are useful in testing or calculating only part
#' of the full outputs from \link[PhiCor]{Indicator.all.plusP}:
#' \itemize{
#' \item \link[PhiCor]{Indicator.nonEqual} Calculated the non group-equalised habitat indicator value (IndVal).
#' \item \link[PhiCor]{Indicator.groupEqual} Calculated the group-equalised species indicator value (IndVal).
#' \item \link[PhiCor]{Indicator.xPerm} Permutates and calculates for each permutation both the non and
#' group-equalised species indicator value (IndVal).
#' \item
#' }
#'
#' The functions \link[PhiCor]{lookup} and \link[PhiCor]{FilePathInTree}
#' might be used elsewhere.
#' @author Jordan Chetcuti
#' @references
#' De Cáceres, M., & Legendre, P. (2009). Associations between species and groups of sites: indices and statistical inference. Ecology, 90(12), 3566–3574.
#' @references
#' 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).
#' @keywords internal
#'
"_PACKAGE"
#> [1] "_PACKAGE"
#' Species 1 data of presence absence and weighting for LCM2015 land cover
#'
#' The data is anonymised data created using LCM2015 \cite{Rowland et al., (2017)} and \cite{NBN Atlas website (2018)}
#'
#' @format A data frame with 1,654 rows and 5 variables:
#' @usage The data is useful in examples of how to use the functions.
#' \describe{
#' \item{LocationID}{A anonymised location}
#' \item{bhab}{LCM2015 land cover description}
#' \item{Proportion}{The proportion of a location that is each habitat}
#' \item{HabId}{An integer ID for each habitat in LCM2015}
#' \item{Species1}{Binary presence absence for "species1"}
#' }
#' @references Rowland, C. S., Morton, R. D., Carrasco, L., McShane, G., O’Neil, A. W., & Wood, C. M. (2017). Land Cover Map 2015 (vector, GB). NERC Environmental Information Data Centre. NERC Environmental Information Data Centre. doi:/10.5285/6c6c9203-7333-4d96-88ab-78925e7a4e73
#' @references NBN Atlas website. (2018). at https://nbnatlas.org. Accessed 13 April 2018.
"Species1"
#' Species 2 data of presence absence and weighting for LCM2015 land cover
#'
#' The data is anonymised data created using LCM2015 \cite{Rowland et al., (2017)} and \cite{NBN Atlas website (2018)}
#'
#' @format A data frame with 1,005 rows and 5 variables:
#' @usage The data is useful in examples of how to use the functions.
#' \describe{
#' \item{LocationID}{A anonymised location}
#' \item{bhab}{LCM2015 land cover description}
#' \item{Proportion}{The proportion of a location that is each habitat}
#' \item{HabId}{An integer ID for each habitat in LCM2015}
#' \item{Species2}{Binary presence absence for "species2"}
#' }
#' @references Rowland, C. S., Morton, R. D., Carrasco, L., McShane, G., O’Neil, A. W., & Wood, C. M. (2017). Land Cover Map 2015 (vector, GB). NERC Environmental Information Data Centre. NERC Environmental Information Data Centre. doi:/10.5285/6c6c9203-7333-4d96-88ab-78925e7a4e73
#' @references NBN Atlas website. (2018). at https://nbnatlas.org. Accessed 13 April 2018.
"Species2"
OutputClass = c(result = data.frame, InVariables = class)
class(OutputClass) = "Output"
print.Output = function(Out){
print(Out$result)
}
default.Output = function(Out){
return(Out$result)
}
#' Lookup function for looking up values from one dataframe for another
#'
#' Works very similarly to vlookup in excel.
#' The output is a field in the dataset you are wanting to copy information into.
#' The syntax is something like:
#' MyDataframe$CopiedField = lookup(MyDataframe$joinField, OtherDataframe$joinField, OutherDataframe$DataToCopy)
#'
#' @param MasterDataField vector - a field with common values to those in another dataset you want to
#' lookup data in.
#' @param AdditionField vector - a field with common values to MasterDataField
#' @param AdditonInputField vector - the data you are importing to the Master dataframe where the
#' AdditionField matches the MasterDataField
#' @return vector - will reurn the vector corrosponding to the MasterDataField that
#' are found in the lookup field AdditonInputField
#' @examples
#' #Lookup DF
#' HabID = c(1, 2, 3, 4, 5, 6)
#' Habitat = c("BLeave", "AGrass", "CGrass", "Water", "Bog", "Heath")
#' Desc = c("Broadleaved and mixed woodland", "Acid grassland","Calcareous grassland", "Inland freshwater", "Sphagnum bog", "Caluna heathland")
#' OpenClosed = c("Closed", "Open", "Open", "Open","Open","Open")
#' DomSpecies = c("Oak", "Bent grass", "Festuca ovina", "NA", "Sphagnum Spp.", "Caluna Spp.")
#' lookupdf = data.frame(HabID, Habitat, Desc, OpenClosed, DomSpecies)
#'
#' #Feild sites dataframe.
#' SiteID = c(1,2,3,4,5,6,7,8,9,10)
#' SiteHabitat = c("BLeave", "AGrass", "BLeave", "AGrass", "Heath", "Bog", "Water", "BLeave", "Water", "Heath")
#' SitesDF = data.frame(SiteID, SiteHabitat)
#'
#' #Looking up the descriptions of the habitats.
#' SitesDF$HabDesc = lookup(SitesDF$SiteHabitat, lookupdf$Habitat, lookupdf$Desc)
#'
#' #You could lookup other information from the lookup table.
#' @export
lookup = function(MasterDataField, AdditionField, AdditionInputField){
index <- match(MasterDataField, AdditionField)
#print(index)
Field = list()
count = 1
for(i in index){
if(!is.na(i)){
Field[count] = as.character(AdditionInputField[i])
}else{
Field[count] = NA
}
count = count + 1
}
return(as.character(Field))
}
#' CorIndex is basic initiation of variables that are the same for
#' all habitats in an analysis of multiple habitats in \link[PhiCor]{PhiCor}.
#'
#' This function is usually called by other function, but could be run
#' seperatly to give many of the variables which go into the equations
#' to calculate the phi coefficent. This is useful if manually checking
#' the calculation.
#'
#' @param InDataframe dataframe - containing the data on species presence absence,
#' habitat and weighting. It also optionally could contain location ID's.
#' @param speciesbinary string - the name of the binary field within InDataframe
#' that contains binary presence absence for a species at the location.
#' @param weighted string - name of habitat weighting field. For example, proportion
#' of habitat, but any weighting with all habitats at a location adding to 1.
#' @param group string - ID of habitats or groups of interest.
#' @param SquareID string - optional ID of each location. Useful later for
#' permutating the locations to calculate p-values.
#' @return a custom class "CorIndexVar" containing all values which are the
#' same for all habitats and don't need to be recalculated for every
#' habitat. Including Nk, nk, the habitats "GroupDF", N, K, Ngp, nkoverNk
#' ng & n. See \cite{De Cáceres and Legendre (2009)}
#' This is reused to give all the results later.
#' @examples
#' #Using the included dataframe Species1
#' Inputs_species1 = CorIndex(InDataframe = Species1, speciesbinary = 'Species1', weighted = 'Proportion', group = 'HabId', 'LocationID')
#' Inputs_species1$nkoverNk
#'
#' #Using the included dataframe Species2
#' #If you wanted the analysis to be non-weighted.
#' Species2Unweight = Species2[which(Species2$Proportion==1),]
#' #This analysis is then the unweighted version of the analysis. SquareID is not needed.
#' Inputs_species2 = CorIndex(InDataframe = Species2Unweight, speciesbinary = 'Species2', weighted = 'Proportion', group = 'HabId')
#' names(Inputs_species2)
#'
#' @author Jordan Chetcuti
#' @references
#' De Cáceres, M., & Legendre, P. (2009). Associations between species and groups of sites: indices and statistical inference. Ecology, 90(12), 3566–3574.
#' @references
#' Chetcuti, J., Kunin, W. E. & Bullock, J. M. A weighting method to improve habitat association analysis: tested on British carabids. Ecography (Cop.). 42, 1395–1404 (2019).
#' @export
CorIndex <- function(InDataframe, speciesbinary,weighted, group, SquareID = NULL){
speciesbinary = InDataframe[,speciesbinary]
weighted = InDataframe[,weighted]
group = InDataframe[,group]
#NextMethod("CorIndex")
CorIndex.default(InDataframe, speciesbinary,weighted, group, SquareID)
}
#' The default method of \link[PhiCor]{CorIndex}.
#'
#' This should not be used independently. See \link[PhiCor]{CorIndex} for usage.
#' @usage NULL
#' @export
CorIndex.default <- function(InDataframe, speciesbinary,weighted, group, SquareID = NULL){
rtrn <- list()
class(rtrn) <- "CorIndexVar"
### Weighting the variables Np and np ( calculated for every habitat as Nk and nk and then
###later individually selected as Np and np)
Nk = aggregate(weighted ~ group, InDataframe, sum)
Nk = setNames(Nk, c("group", "Nk"))
nk = aggregate(speciesbinary * weighted ~ group, InDataframe, sum)
nk = setNames(nk, c("group", "nk"))
rtrn$Nk = Nk
rtrn$nk = nk
GroupDF = Nk
GroupDF[,2] =NULL
rtrn$GroupDF = GroupDF
N = sum(weighted)
rtrn$N = N
K = length(Nk$group)
rtrn$K = K
Ngp = N/K
rtrn$Ngp = Ngp
nkoverNk = nk/Nk
nkoverNk = setNames(nkoverNk, c("group", "nkoverNk"))
rtrn$nkoverNk = nkoverNk
sumnkoverNk = sum(nkoverNk$nkoverNk)
rtrn$sumnkoverNk = sumnkoverNk
ng = Ngp*sumnkoverNk
rtrn$ng = ng
n = sum(nk$nk)
rtrn$n = n
return(rtrn)
}
#' CorIndex.TargetVar is creates the variables that are particular to a
#' habitat that were not created in the \link[PhiCor]{CorIndex}
#'
#' This function is usually called by other function, but could be run
#' seperatly to give many of the variables which go into the equations
#' to calculate the phi coefficent. This is useful if manually checking
#' the calculation.
#'
#' @param CorIndexVarInput is the custom class "CorIndexVar" output from \link[PhiCor]{CorIndex}
#' @param targetgroup Integer - ID of the habitat of interest.
#' @return a custom class "CorIndexVar" containing all values which are the
#' same for all habitats and don't need to be recalculated for every
#' habitat and the ones for the specific target habitat. Including those from
#' CorIndex Nk, nk, the habitats "GroupDF", N, K, Ngp, nkoverNk ng & n and
#' aditionally np, Np & ngp See \cite{De Cáceres and Legendre (2009)}
#' This is reused to give all the results later.
#'
#' @examples
#' #Builing on the examples from \link[PhiCor]{CorIndex}
#' #Using the included dataframe Species1
#' Inputs_species1 = CorIndex(InDataframe = Species1, speciesbinary = 'Species1', weighted = 'Proportion', group = 'HabId', 'LocationID')
#' Inputs_species1$nkoverNk
#' #Using CorIndex.TargetVar, the input is the output of CorIndex. In this case we are interested in
#' #habitat 1 (using the ID for the habitat)
#' Inputs_species1_hab1 = CorIndex.TargetVar(Inputs_species1, 1)
#'
#'
#' #Using the included dataframe Species2
#' #If you wanted the analysis to be non-weighted.
#' Species2Unweight = Species2[which(Species2$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)
#'
#'
#' @seealso \link[PhiCor]{CorIndex}
#' @author Jordan Chetcuti
#' @references
#' De Cáceres, M., & Legendre, P. (2009). Associations between species and groups of sites: indices and statistical inference. Ecology, 90(12), 3566–3574.
#' @references
#' 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).
#' @export
CorIndex.TargetVar <- function(CorIndexVarInput, targetgroup){
rtrn <- list()
class(rtrn) <- "CorIndexVar"
rtrn = CorIndexVarInput
nk = CorIndexVarInput$nk
Nk = CorIndexVarInput$Nk
Ngp = CorIndexVarInput$Ngp
np = nk$nk[nk$group==targetgroup]
Np = Nk$Nk[Nk$group==targetgroup]
rtrn$np = np
rtrn$Np = Np
ngp = Ngp*np/Np
rtrn$ngp = ngp
return(rtrn)
}
#' Calculated the group-equalised Phi Coefficient of correlation.
#'
#' This function calculates the group-equalised version of the Phi coefficient of
#' correlation \cite{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.
#'
#' @param CorIndexTargetVarInput a custom class "CorIndexVar" containing all values from
#' from both \link[PhiCor]{CorIndex} and \link[PhiCor]{CorIndex.TargetVar}
#' @return Phi - a floating point number between -1. and 1.
#' @examples
#' #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)
#'
#'
#' @seealso \link[PhiCor]{PhiCor}
#' @author Jordan Chetcuti
#' @references
#' Tichy, L., & Chytry, M. (2006). Statistical determination of diagnostic species for site groups of unequal size. Journal of Vegetation Science, 17(6), 809–818.
#' @references
#' De Cáceres, M., & Legendre, P. (2009). Associations between species and groups of sites: indices and statistical inference. Ecology, 90(12), 3566–3574.
#' @references
#' 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).
#' @export
CorIndex.groupEqual <- function(CorIndexTargetVarInput){
N = CorIndexTargetVarInput$N
ngp = CorIndexTargetVarInput$ngp
ng = CorIndexTargetVarInput$ng
Ngp = CorIndexTargetVarInput$Ngp
Requ = (N*ngp - ng*Ngp)/sqrt((N*ng - ng^2)*(N*Ngp - Ngp^2))
return(Requ)
}
#' Calculated the non group-equalised Phi Coefficient of correlation.
#'
#' This function calculates the non group-equalised version of the Phi coefficient of
#' correlation \cite{De Cáceres and Legendre (2009)}. This is
#' useful alone to calculate the non group-equalised version for a single habitat of interest
#' among multiple other habitats.
#'
#' @param CorIndexTargetVarInput a custom class "CorIndexVar" containing all values from
#' from both \link[PhiCor]{CorIndex} and \link[PhiCor]{CorIndex.TargetVar}
#' @return Phi - a floating point number between -1. and 1.
#' @examples
#' #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$nkoverNk
#' #Using CorIndex.TargetVar, the input is the output of CorIndex. In this case we are interested in
#' #habitat 1 (using the ID for the habitat)
#' Inputs_species1_hab1 = CorIndex.TargetVar(Inputs_species1, 1)
#'
#' species1_hab1_Phi = CorIndex.nonEqual(Inputs_species1_hab1)
#'
#'
#'
#' #Using the included dataframe Species2
#' #If you wanted the analysis to be non-weighted.
#' Species2Unweight = Species2[which(Species2$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_Phi = CorIndex.nonEqual(Inputs_species2_hab4)
#' @seealso \link[PhiCor]{PhiCor}
#' @author 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.
#' @references
#' De Cáceres, M., & Legendre, P. (2009). Associations between species and groups of sites: indices and statistical inference. Ecology, 90(12), 3566–3574.
#' @references
#' 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).
#' @export
CorIndex.nonEqual <- function(CorIndexTargetVarInput){
N = CorIndexTargetVarInput$N
np = CorIndexTargetVarInput$np
n = CorIndexTargetVarInput$n
Np = CorIndexTargetVarInput$Np
Rnon = (N*np - n*Np)/sqrt((N*n - n^2)*(N*Np - Np^2))
return(Rnon)
}
#' Calculated both the non and group-equalised Phi Coefficient of correlation for all habitats.
#'
#' This function calculates the non and group-equalised versions of the Phi coefficient of
#' correlation \cite{Tichy and Chytry (2006), De Cáceres and Legendre (2009)}.
#'
#' It doesn't calculate any p-values by permutation and therefore calculates very quickly
#' (unlike the permutated version). It is therefore a useful first step and/or useful
#' when the p-values aren't important.
#'
#' @param CorIndexVarInput a custom class "CorIndexVar" containing all values from
#' from only \link[PhiCor]{CorIndex}. It calls \link[PhiCor]{CorIndex.TargetVar} within
#' the function.
#' @return dataframe of habitat ID and non equalised (R) and equalised (Rg) Phi values
#' the R and Rg numbers are both floating point number between -1. and 1.
#' @examples
#' #Builing on the examples from \link[PhiCor]{CorIndex}
#'
#' #Using the included dataframe Species1
#' Inputs_species1 = CorIndex(InDataframe = Species1, speciesbinary = 'Species1', weighted = 'Proportion', group = 'HabId', 'LocationID')
#' Inputs_species1$nkoverNk
#' Species1_AllPhi = CorIndex.all(Inputs_species1)
#'
#'
#'
#' #Using the included dataframe Species2
#' #If you wanted the analysis to be non-weighted.
#' Species2Unweight = Species2[which(Species2$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)
#' Species2_AllPhi = CorIndex.all(Inputs_species2)
#'
#'
#' @seealso \link[PhiCor]{PhiCor}
#' @author Jordan Chetcuti
#' @references
#' Tichy, L., & Chytry, M. (2006). Statistical determination of diagnostic species for site groups of unequal size. Journal of Vegetation Science, 17(6), 809–818.
#' @references
#' De Cáceres, M., & Legendre, P. (2009). Associations between species and groups of sites: indices and statistical inference. Ecology, 90(12), 3566–3574.
#' @references
#' 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).
#' @export
CorIndex.all <- function(CorIndexVarInput){
OutDF = CorIndexVarInput$GroupDF
OutDF$R = 0.0000
OutDF$Rg = 0.0000
for(i in 1:nrow(OutDF)) {
targetVariables = CorIndex.TargetVar(CorIndexVarInput,OutDF[i,1])
print(OutDF[i,1])
OutDF[i,2]= CorIndex.nonEqual(targetVariables)
OutDF[i,3]= CorIndex.groupEqual(targetVariables)
}
return(OutDF)
}
CorIndex.APerm <- function(InDataframe, FieldToPerm, SquareID = NULL){
copyDF = InDataframe
if(is.null(SquareID)){
copyDF[,FieldToPerm] = sample(copyDF[,FieldToPerm], nrow(copyDF), replace = FALSE, prob = NULL)
}else{
AggCopyDF = aggregate(InDataframe[,FieldToPerm] ~ InDataframe[,SquareID], InDataframe, min)
AggCopyDF = setNames(AggCopyDF, c(SquareID, FieldToPerm))
AggCopyDF[,FieldToPerm] = sample(AggCopyDF[,FieldToPerm], nrow(AggCopyDF), replace = FALSE, prob = NULL)
copyDF[,FieldToPerm] = as.numeric(lookup(copyDF[,SquareID], AggCopyDF[,SquareID], AggCopyDF[,2]))
}
return(copyDF)
}
CorIndex.toroidalAPerm <- function(InDataframe, FieldToPerm, SquareID = NULL){
copyDF = InDataframe
if(is.null(SquareID)){
rows = nrow(copyDF)
MoveOn = sample(0:rows, 1)
copyDF$Index = row.names(copyDF)
copyDF$Index = as.numeric(copyDF$Index) + MoveOn
copyDF$Index = ifelse(copyDF$Index>rows,copyDF$Index - rows, copyDF$Index )
temp = copyDF[,c(FieldToPerm, "Index")]
temp = temp[order(temp$Index),]
copyDF[,FieldToPerm] = temp[,FieldToPerm]
copyDF$Index = NULL
#copyDF[,FieldToPerm] = sample(copyDF[,FieldToPerm], nrow(copyDF), replace = FALSE, prob = NULL)
}else{
AggCopyDF = aggregate(InDataframe[,FieldToPerm] ~ InDataframe[,SquareID], InDataframe, min)
AggCopyDF = setNames(AggCopyDF, c(SquareID, FieldToPerm))
rows = nrow(AggCopyDF)
MoveOn = sample(0:rows, 1)
AggCopyDF$Index = row.names(AggCopyDF)
AggCopyDF$Index = as.numeric(AggCopyDF$Index) + MoveOn
AggCopyDF$Index = ifelse(AggCopyDF$Index>rows,AggCopyDF$Index - rows, AggCopyDF$Index )
temp = AggCopyDF[,c(FieldToPerm, "Index")]
temp = temp[order(temp$Index),]
AggCopyDF[,FieldToPerm] = temp[,FieldToPerm]
AggCopyDF$Index = NULL
AggCopyDF[,FieldToPerm] = sample(AggCopyDF[,FieldToPerm], nrow(AggCopyDF), replace = FALSE, prob = NULL)
copyDF[,FieldToPerm] = as.numeric(lookup(copyDF[,SquareID], AggCopyDF[,SquareID], AggCopyDF[,2]))
}
return(copyDF)
}
#' Permutates and calculates for each permutation both the non and group-equalised
#' Phi coefficent.
#'
#' This function purmutaes and calculates the non and group-equalised versions of the Phi coefficient of
#' correlation \cite{Tichy and Chytry (2006), De Cáceres and Legendre (2009)}.
#' It is useful as it gives the distribution of phi values if the species are distributed
#' randomly across the locations, the phi value for the actual data is compared to this
#' distribution to calculate the p-value. The distribution is a useful output to visualise
#' the distribution.
#'
#' @param InDataframe dataframe - containing the data on species presence absence,
#' habitat and weighting. It also optionally could contain location ID's.
#' @param FieldToPerm string - the name of the binary field within InDataframe
#' that contains binary presence absence for a species at the location.
#' @param weighted string - name of habitat weighting field. For example, proportion
#' of habitat, but any weighting with all habitats at a location adding to 1.
#' @param groupsField string - ID of habitats or groups of interest.
#' @param targetgroup Integer - ID of the habitat of interest.
#' @param x Integer - optional - the number of permutation, set as 1000 by default.
#' @param 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.
#' @param toroidal Boolean - If you suspect that your data is spatially auto-correlated you should use
#' a toroidal permutation.
#' @return Two data frames, one for the group-equalised $`GroupEqualPermList` and one
#' for the non group-equalised $NonGroupEqualPermList. It also returns the number of
#' permutations $Permutations.
#' @examples
#' #Builing on the examples from \link[PhiCor]{CorIndex.nonEqual}
#' #Using the included dataframe Species1
#' Inputs_species1 = CorIndex(InDataframe = Species1, speciesbinary = 'Species1', weighted = 'Proportion', group = 'HabId', 'LocationID')
#' Inputs_species1$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_Phi = CorIndex.nonEqual(Inputs_species1_hab1)
#'
#' #histogram showing the output of CorIndex.xPerm for habitat 1 in comparison to the phi value from actual data.
#' hist(Hab1_PermutatedPhi$NonGroupEqualPermList, main = "Non group-equalised distribution", xlab = "Phi values", breaks = 100)
#' abline(v=species1_hab1_Phi, col="red")
#' text((species1_hab1_Phi + 0.006), 15, "Calulated phi value", col = "red", srt=90)
#'
#'
#'
#' @seealso \link[PhiCor]{PhiCor}
#' @author Jordan Chetcuti
#' @export
CorIndex.xPerm <- function(InDataframe, FieldToPerm, weighted, groupsField, targetGroup, x = 1000, SquareID = NULL, toroidal = FALSE){
GroupEqualPermList = c()
NonGroupEqualPermList = c()
for(i in 1:x){
if (toroidal){
Perm1 = CorIndex.toroidalAPerm(InDataframe, FieldToPerm, SquareID)
}else{
Perm1 = CorIndex.APerm(InDataframe, FieldToPerm, SquareID)
}
UnchangePerm = CorIndex(Perm1, FieldToPerm, weighted, groupsField)
VarPerm1 = CorIndex.TargetVar(UnchangePerm, targetGroup)
GroupEqualPermList = c(GroupEqualPermList, CorIndex.groupEqual(VarPerm1))
NonGroupEqualPermList = c(NonGroupEqualPermList, CorIndex.nonEqual(VarPerm1))
}
out = list("GroupEqualPermList" = GroupEqualPermList, "NonGroupEqualPermList" = NonGroupEqualPermList, "Permutations" = x)
return(out)
}
#' Calculated both the non and group-equalised Phi Coefficient of correlation for all habitats
#' and the p-values.
#'
#' This function calculates the non and group-equalised versions of the Phi coefficient of
#' correlation \cite{Tichy and Chytry (2006), De Cáceres and Legendre (2009)}.
#'
#' 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.
#'
#' @param InDataframe dataframe - containing the data on species presence absence,
#' habitat and weighting. It also optionally could contain location ID's.
#' @param speciesbinary string - the name of the binary field within InDataframe
#' that contains binary presence absence for a species at the location.
#' @param weighted string - name of habitat weighting field. For example, proportion
#' of habitat, but any weighting with all habitats at a location adding to 1.
#' @param group string - ID of habitats or groups of interest.
#' @param numberIteration Integer - optional - the number of permutation, set as 1000 by default.
#' @param 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.
#' @param toroidal Boolean - If you suspect that your data is spatially auto-correlated you should use
#' a toroidal permutation.
#' @return 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 \cite{De Cáceres and Legendre (2009)}. To this is added another custom
#' class "Output" as $`result` which gives the habitat ID, R (non group-equalised phi)
#' pR (the p-value for R), Rg (group-equalised phi) and pRg (the p-value for Rg). This
#' "Output" class is the main result, the CorIndexVar is included as metadata of the
#' input values.
#' @examples
#' #Using the included dataframe Species1
#' Species1_AllPhiPvalues = CorIndex.all.plusP(InDataframe = Species1, speciesbinary = "Species1", weighted = 'Proportion', group = 'HabId', SquareID = 'LocationID')
#' names(Species1_AllPhiPvalues)
#' Species1_AllPhiPvalues$`result`
#' @seealso \link[PhiCor]{PhiCor}
#' @author Jordan Chetcuti
#' @references
#' Tichy, L., & Chytry, M. (2006). Statistical determination of diagnostic species for site groups of unequal size. Journal of Vegetation Science, 17(6), 809–818.
#' @references
#' De Cáceres, M., & Legendre, P. (2009). Associations between species and groups of sites: indices and statistical inference. Ecology, 90(12), 3566–3574.
#' @references
#' 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).
#' @export
CorIndex.all.plusP <- function(InDataframe, speciesbinary, weighted, group, numberIteration = 1000, SquareID = NULL, toroidal = FALSE){
CorIndexVarInput = CorIndex(InDataframe, speciesbinary, weighted, group, SquareID)
OutDF = CorIndexVarInput$GroupDF
OutDF$R = 0.0000000000
OutDF$pR = 0.0000000000
OutDF$Rg = 0.0000000000
OutDF$pRG = 0.0000000000
for(i in 1:nrow(OutDF)) {
targetVariables = CorIndex.TargetVar(CorIndexVarInput,OutDF[i,1])
print(OutDF[i,1])
Distributions = CorIndex.xPerm(InDataframe, speciesbinary,weighted,group,OutDF[i,"group"], numberIteration, SquareID, toroidal)
OutDF[i,2]= CorIndex.nonEqual(targetVariables)
#Calc each of the probabilities.
length(Distributions$NonGroupEqualPermList[Distributions$NonGroupEqualPermList >= OutDF[i,2]])/numberIteration
length(Distributions$NonGroupEqualPermList[Distributions$NonGroupEqualPermList <= OutDF[i,2]])/numberIteration
OutDF[i,3] = min(length(Distributions$NonGroupEqualPermList[Distributions$NonGroupEqualPermList >= OutDF[i,2]])/numberIteration , length(Distributions$NonGroupEqualPermList[Distributions$NonGroupEqualPermList <= OutDF[i,2]])/numberIteration)
OutDF[i,4]= CorIndex.groupEqual(targetVariables)
OutDF[i,5] = min(length(Distributions$GroupEqualPermList[Distributions$GroupEqualPermList >= OutDF[i,4]])/numberIteration , length(Distributions$GroupEqualPermList[Distributions$GroupEqualPermList <= OutDF[i,4]])/numberIteration)
OutputClass$result = OutDF
OutputClass$InVariables = CorIndexVarInput
}
return(OutputClass)
}
#' Calculated the non group-equalised habitat indicator value (IndVal).
#'
#' This function calculates the non group-equalised version of the habitat indicator value (IndVal)
#' \cite{De Cáceres and Legendre (2009)}. This is
#' useful alone to calculate the non group-equalised version for a single habitat of interest
#' among multiple other habitats.
#'
#' @param CorIndexTargetVarInput a custom class "CorIndexVar" containing all values from
#' from both \link[PhiCor]{CorIndex} and \link[PhiCor]{CorIndex.TargetVar}
#' @return IndVal - a floating point number between 0 and 1.
#' @examples
#' #Builing on the examples from \link[PhiCor]{CorIndex.TargetVar}
#' #Using the included dataframe Species1
#' Inputs_species1 = CorIndex(InDataframe = Species1, speciesbinary = 'Species1', weighted = 'Proportion', group = 'HabId', 'LocationID')
#' Inputs_species1$nkoverNk
#' #Using CorIndex.TargetVar, the input is the output of Indicator. In this case we are interested in
#' #habitat 1 (using the ID for the habitat)
#' Inputs_species1_hab1 = Indicator.TargetVar(Inputs_species1, 1)
#'
#' species1_hab1_Phi = Indicator.nonEqual(Inputs_species1_hab1)
#'
#'
#'
#' #Using the included dataframe Species2
#' #If you wanted the analysis to be non-weighted.
#' Species2Unweight = Species2[which(Species2$Proportion==1),]
#' #This analysis is then the unweighted version of the analysis. SquareID is not needed.
#' Inputs_species2 = CorIndex(InDataframe = Species2Unweight, speciesbinary = 'Species2', weighted = 'Proportion', group = 'HabId')
#' names(Inputs_species2)
#'
#' #Looking at habitat 4
#' Inputs_species2_hab4 = Indicator.TargetVar(Inputs_species2, 4)
#'
#' species2_hab4_IndVal = Indicator.nonEqual(Inputs_species2_hab4)
#' @seealso \link[PhiCor]{PhiCor}
#' @author Jordan Chetcuti
#' @references
#' De Cáceres, M., & Legendre, P. (2009). Associations between species and groups of sites: indices and statistical inference. Ecology, 90(12), 3566–3574.
#' @references
#' Chetcuti, J., Kunin, W. E. & Bullock, J. M. A weighting method to improve habitat association analysis: tested on British carabids. Ecography (Cop.). 42, 1395–1404 (2019).
#' @export
Indicator.nonEqual <- function(CorIndexTargetVarInput){
N = CorIndexTargetVarInput$N
np = CorIndexTargetVarInput$np
n = CorIndexTargetVarInput$n
Np = CorIndexTargetVarInput$Np
########
Rnon = sqrt((np/n)*(np/Np))
return(Rnon)
}
#' Calculated the group-equalised species indicator value (IndVal).
#'
#' This function calculates the group-equalised version of the species indicator value (IndVal)
#' \cite{De Cáceres and Legendre (2009)}. This is
#' useful alone to calculate the group-equalised version for a single habitat of interest
#' among multiple other habitats.
#'
#' @param CorIndexTargetVarInput a custom class "CorIndexVar" containing all values from
#' from both \link[PhiCor]{CorIndex} and \link[PhiCor]{CorIndex.TargetVar}
#' @return IndVal - a floating point number between 0 and 1.
#' @examples
#' #Builing on the examples from \link[PhiCor]{CorIndex.TargetVar}
#' #Using the included dataframe Species1
#' Inputs_species1 = CorIndex(InDataframe = Species1, speciesbinary = 'Species1', weighted = 'Proportion', group = 'HabId', 'LocationID')
#' Inputs_species1$nkoverNk
#' #Using CorIndex.TargetVar, the input is the output of Indicator. 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_IndVal = Indicator.groupEqual(Inputs_species1_hab1)
#'
#'
#'
#' #Using the included dataframe Species2
#' #If you wanted the analysis to be non-weighted.
#' Species2Unweight = Species2[which(Species2$Proportion==1),]
#' #This analysis is then the unweighted version of the analysis. SquareID is not needed.
#' Inputs_species2 = CorIndex(InDataframe = Species2Unweight, speciesbinary = 'Species2', weighted = 'Proportion', group = 'HabId')
#' names(Inputs_species2)
#'
#' #Looking at habitat 4
#' Inputs_species2_hab4 = Indicator.TargetVar(Inputs_species2, 4)
#'
#' species2_hab4_GE_IndVal = Indicator.groupEqual(Inputs_species2_hab4)
#'
#'
#' @seealso \link[PhiCor]{PhiCor}
#' @author Jordan Chetcuti
#' @references
#' De Cáceres, M., & Legendre, P. (2009). Associations between species and groups of sites: indices and statistical inference. Ecology, 90(12), 3566–3574.
#' @references
#' Chetcuti, J., Kunin, W. E. & Bullock, J. M. A weighting method to improve habitat association analysis: tested on British carabids. Ecography (Cop.). 42, 1395–1404 (2019).
#' @export
Indicator.groupEqual <- function(CorIndexTargetVarInput){
N = CorIndexTargetVarInput$N
ngp = CorIndexTargetVarInput$ngp
ng = CorIndexTargetVarInput$ng
Ngp = CorIndexTargetVarInput$Ngp
SumNks = sum(CorIndexTargetVarInput$nk/CorIndexTargetVarInput$Nk)
################
Requ = sqrt(((ngp/Ngp)/SumNks)*(ngp/Ngp))
return(Requ)
}
#' Permutates and calculates for each permutation both the non and group-equalised
#' species indicator value (IndVal).
#'
#' This function purmutaes and calculates the non and group-equalised versions of species indicator
#' value (IndVal) \cite{De Cáceres and Legendre (2009)}.
#' It is useful as it gives the distribution of IndVal values if the species are distributed
#' randomly across the locations, the IndVal value for the actual data is compared to this
#' distribution to calculate the p-value. The distribution is a useful output to visualise
#' the distribution.
#'
#' @param InDataframe dataframe - containing the data on species presence absence,
#' habitat and weighting. It also optionally could contain location ID's.
#' @param FieldToPerm string - the name of the binary field within InDataframe
#' that contains binary presence absence for a species at the location.
#' @param weighted string - name of habitat weighting field. For example, proportion
#' of habitat, but any weighting with all habitats at a location adding to 1.
#' @param groupsField string - ID of habitats or groups of interest.
#' @param targetgroup Integer - ID of the habitat of interest.
#' @param x Integer - optional - the number of permutation, set as 1000 by default.
#' @param 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.
#' @param toroidal Boolean - If you suspect that your data is spatially auto-correlated you should use
#' a toroidal permutation.
#' @return Two data frames, one for the group-equalised $`GroupEqualPermList` and one
#' for the non group-equalised $NonGroupEqualPermList. It also returns the number of
#' permutations $Permutations.
#' @examples
#' #Building on the examples from \link[PhiCor]{Indicator.nonEqual}
#' #Using the included dataframe Species1
#' Inputs_species1 = CorIndex(InDataframe = Species1, speciesbinary = 'Species1', weighted = 'Proportion', group = 'HabId', 'LocationID')
#' Inputs_species1$nkoverNk
#' #Using CorIndex.TargetVar, the input is the output of Indicator. 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_Indval = Indicator.nonEqual(Inputs_species1_hab1)
#'
#' #histogram showing the output of Indicator.xPerm for habitat 1 in comparison to the IndVal value from actual data.
#' hist(Hab1_PermutatedPhi$NonGroupEqualPermList, main = "Non group-equalised distribution", xlab = "IndVal values", breaks = 100)
#' abline(v=species1_hab1_IndVal, col="red")
#' text((species1_hab1_IndVal + 0.006), 15, "Calulated IndVal value", col = "red", srt=90)
#'
#'
#'
#' @seealso \link[PhiCor]{PhiCor}
#' @author Jordan Chetcuti
#' @export
Indicator.xPerm <- function(InDataframe, FieldToPerm, weighted, groupsField, targetGroup, x = 1000, SquareID = NULL, toroidal = FALSE){
GroupEqualPermList = c()
NonGroupEqualPermList = c()
for(i in 1:x){
if (toroidal){
Perm1 = CorIndex.toroidalAPerm(InDataframe, FieldToPerm, SquareID)
}else{
Perm1 = CorIndex.APerm(InDataframe, FieldToPerm, SquareID)
}
UnchangePerm = CorIndex(Perm1, FieldToPerm, weighted, groupsField)
VarPerm1 = CorIndex.TargetVar(UnchangePerm, targetGroup)
#########
GroupEqualPermList = c(GroupEqualPermList, Indicator.groupEqual(VarPerm1))
NonGroupEqualPermList = c(NonGroupEqualPermList, Indicator.nonEqual(VarPerm1))
}
out = list("GroupEqualPermList" = GroupEqualPermList, "NonGroupEqualPermList" = NonGroupEqualPermList, "Permutations" = x)
return(out)
}
#' Calculated both the non and group-equalised IndVal for all habitats
#' and the p-values.
#'
#' This function calculates the non and group-equalised versions of an habitat indicator value (IndVal)
#' correlation \cite{De Cáceres and Legendre (2009)}.
#'
#' It calculate p-values by permutation and therefore calculates take a bit of time.
#'
#' @param InDataframe dataframe - containing the data on species presence absence,
#' habitat and weighting. It also optionally could contain location ID's.
#' @param speciesbinary string - the name of the binary field within InDataframe
#' that contains binary presence absence for a species at the location.
#' @param weighted string - name of habitat weighting field. For example, proportion
#' of habitat, but any weighting with all habitats at a location adding to 1.
#' @param group string - ID of habitats or groups of interest.
#' @param numberIteration Integer - optional - the number of permutation, set as 1000 by default.
#' @param 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.
#' @param toroidal Boolean - If you suspect that your data is spatially auto-correlated you should use
#' a toroidal permutation.
#' @return 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 \cite{De Cáceres and Legendre (2009)}. To this is added another custom
#' class "Output" as $`result` which gives the habitat ID, I (non group-equalised IndVal)
#' pI (the p-value for I), Ig (group-equalised IndVal) and Ig (the p-value for Ig). This
#' "Output" class is the main result, the CorIndexVar is included as metadata of the
#' input values.
#' @examples
#' #Using the included dataframe Species1
#' Species1_AllIndValvalues = Indicator.all.plusP(InDataframe = Species1, speciesbinary = "Species1", weighted = 'Proportion', group = 'HabId', SquareID = 'LocationID')
#' names(Species1_AllIndValvalues)
#' Species1_AllIndValvalues$`result`
#' @seealso \link[PhiCor]{PhiCor}
#' @author Jordan Chetcuti
#' @references
#' De Cáceres, M., & Legendre, P. (2009). Associations between species and groups of sites: indices and statistical inference. Ecology, 90(12), 3566–3574.
#' @references
#' 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).
#' @export
Indicator.all.plusP <- function(InDataframe, speciesbinary, weighted, group, numberIteration = 1000, SquareID = NULL, toroidal = FALSE){
CorIndexVarInput = CorIndex(InDataframe, speciesbinary, weighted, group, SquareID)
OutDF = CorIndexVarInput$GroupDF
OutDF$I = 0.0000000000
OutDF$pI = 0.0000000000
OutDF$Ig = 0.0000000000
OutDF$pIG = 0.0000000000
for(i in 1:nrow(OutDF)) {
targetVariables = CorIndex.TargetVar(CorIndexVarInput,OutDF[i,1])
print(OutDF[i,1])
Distributions = Indicator.xPerm(InDataframe, speciesbinary,weighted,group,i, numberIteration, SquareID, toroidal)
OutDF[i,2]= Indicator.nonEqual(targetVariables)
#Calc each of the probabilities.
length(Distributions$NonGroupEqualPermList[Distributions$NonGroupEqualPermList >= OutDF[i,2]])/numberIteration
length(Distributions$NonGroupEqualPermList[Distributions$NonGroupEqualPermList <= OutDF[i,2]])/numberIteration
OutDF[i,3] = min(length(Distributions$NonGroupEqualPermList[Distributions$NonGroupEqualPermList >= OutDF[i,2]])/numberIteration , length(Distributions$NonGroupEqualPermList[Distributions$NonGroupEqualPermList <= OutDF[i,2]])/numberIteration)
OutDF[i,4]= Indicator.groupEqual(targetVariables)
OutDF[i,5] = min(length(Distributions$GroupEqualPermList[Distributions$GroupEqualPermList >= OutDF[i,4]])/numberIteration , length(Distributions$GroupEqualPermList[Distributions$GroupEqualPermList <= OutDF[i,4]])/numberIteration)
OutputClass$result = OutDF
OutputClass$InVariables = CorIndexVarInput
}
return(OutputClass)
}
#' Looks for a filename in the current folder and then expands up and along the file tree
#' looking for the filename.
#'
#' This isn't really part of calculating the Phi coefficient, but is useful in finding a
#' file which has been reorganised within a project. Imperfect as will return multiple
#' paths if the filename is used twice locally.
#'
#' @param StringFileName string filename
#' @return Filepath including the input StringFileName for all files found in the vicinity.
#' If it finds multiple files it returns a list of paths which can be accessed individually
#' using [1], [2]...etc.
#' @examples
#' #Will return as "character" all R related files in the directory you are currently working on
#' PathRfiles = FilePathInTree(".R")
#' #If there is only one file PathRfiles would return it, but with multiple PathRfiles[1] would return the first or only.
#' PathRfiles[1]
#'
#' @export
FilePathInTree = function(StringFileName){
OldPath = getwd()
while(length(dir(getwd(), StringFileName,recursive=TRUE))==0){
setwd('..')
}
FilePath = file.path(getwd(),dir(getwd(), StringFileName,recursive=TRUE))
setwd(OldPath)
rm(OldPath)
return(FilePath)
}
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