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Jordan Chetcuti edited this page Aug 29, 2018 · 1 revision

\documentclass{article} \usepackage[utf8]{inputenc} \usepackage[colorlinks]{hyperref} \usepackage{listings} \title{PhiCor R package} \author{Jordan Chetcuti } \date{August 2018}

\begin{document}

\maketitle

\section{Introduction to PhiCor} \label{sec:PhiCor} PhiCor is a package for calculation the Weighted Phi Coefficient of association This package can be used to calculate the weighted phi coefficient of association using a weighting for habitats within a location, or can calculate the unweighted original version from 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, orsite fidelity of habitat sites.

\subsection{Details} The main function in PhiCor is CorIndex.all.plusP}. This will calculate all phi corelation of association values and purmutate to give p-values. \newline \The other Phi coefficent functions are useful in testing or calculating only part of the full outputs from CorIndex.all.plusP}: \itemize{ \item CorIndex} calculates the inputs that don't change between habitats. \item CorIndex.TargetVar} calculates the additional input specific to a habitat. \item CorIndex.groupEqual} calculates the group-equalised phi for a habitat. \item CorIndex.nonEqual} calculates the non group-equalised phi for a habitat. \item CorIndex.xPerm} returns the null phi distribution for a habitat. \item CorIndex.all} returns the non and group-equalised phi values for all habitats without p-values and is therefore quicker. }

\newline The functions lookup} and FilePathInTree} might be used elsewhere.

\section{Data} Two data frames are included within PhiCor %\hyperref[sec:PhiCor}{PhiCor}.

\subsection{Species1} Species 1 data of presence absence and weighting for LCM2015 land cover

The data is anonymised data created using LCM2015 Rowland et al., (2017)} and 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"} }

\subsection{Species 2} Species 2 data of presence absence and weighting for LCM2015 land cover

The data is anonymised data created using LCM2015 Rowland et al., (2017)} and 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"} }

\section{Lookup function for looking up values from one data frame from 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) \subsubsection*{Parameters} \begin{description} \item [MasterDataField] vector - a field with common values to those in another dataset you want to lookup data in. \item [AdditionField] vector - a field with common values to MasterDataField \item [AdditonInputField] vector - the data you are importing to the Master dataframe where the AdditionField matches the MasterDataField \end{description} \subsubsection*{Returns} Vector - will reurn the vector corrosponding to the MasterDataField that are found in the lookup field AdditonInputField \subsubsection*{Example} \begin{lstlisting}[language=R] #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. \end{lstlisting}

CorIndex is basic initiation of variables that are the same for all habitats in an analysis of multiple habitats in 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 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 Future publication... @export CorIndex <- function(InDataframe, speciesbinary,weighted, group, SquareID = NULL){ speciesbinary = InDataframe[,speciesbinary] weighted = InDataframe[,weighted] group = InDataframe[,group] NextMethod("CorIndex") }

The default method of CorIndex}.

This should not be used independently. See 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 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 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 De Cáceres and Legendre (2009)} This is reused to give all the results later.

@examples #Builing on the examples from 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 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 Future publication... @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 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 CorIndex} and CorIndex.TargetVar} @return Phi - a floating point number between -1. and 1. @examples #Builing on the examples from 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 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 Future publication... @export CorIndex.groupEqual <- function(CorIndexTargetVarInput){ N = CorIndexTargetVarInput$N ngp = CorIndexTargetVarInput$ngp ng = CorIndexTargetVarInput$ng Ngp = CorIndexTargetVarInput$Ngp Requ = (Nngp - ngNgp)/sqrt((Nng - 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 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 CorIndex} and CorIndex.TargetVar} @return Phi - a floating point number between -1. and 1. @examples #Builing on the examples from 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 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 Future publication... @export CorIndex.nonEqual <- function(CorIndexTargetVarInput){ N = CorIndexTargetVarInput$N np = CorIndexTargetVarInput$np n = CorIndexTargetVarInput$n Np = CorIndexTargetVarInput$Np Rnon = (Nnp - nNp)/sqrt((Nn - 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 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 CorIndex}. It calls 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 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 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 Future publication... @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) }

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 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. @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 $Permutations. @examples #Builing on the examples from CorIndex.nonEqual} #Using the included dataframe Species1 Inputs_species1 = CorIndex(InDataframe = Species1, speciesbinary = 'Species1', weighted = 'Proportion', group = 'HabId', 'LocationID') Inputs_species1$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 PhiCor} @author Jordan Chetcuti @export CorIndex.xPerm <- function(InDataframe, FieldToPerm, weighted, groupsField, targetGroup, x = 1000, SquareID = NULL){ GroupEqualPermList = c() NonGroupEqualPermList = c() for(i in 1:x){ 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 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. @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 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 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 Future publication... @export CorIndex.all.plusP <- function(InDataframe, speciesbinary, weighted, group, numberIteration = 1000, SquareID = NULL){ CorIndexVarInput = CorIndex(InDataframe, speciesbinary, weighted, group, SquareID) OutDF = CorIndexVarInput$GroupDF OutDF$R = 0.0000000000 OutDF$pR = 0.0000000000 OutDF$Rg = 0.0000000000 OutDF$pRG = 0.0000000000 for(i in 1:nrow(OutDF)) { targetVariables = CorIndex.TargetVar(CorIndexVarInput,OutDF[i,1]) print(OutDF[i,1]) Distributions = CorIndex.xPerm(InDataframe, speciesbinary,weighted,group,i, numberIteration, SquareID) OutDF[i,2]= CorIndex.nonEqual(targetVariables) #Calc each of the probabilities. length(Distributions$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) }

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) } \end{document}

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