# R/TabCoord.R In robCompositions: Compositional Data Analysis

#### Documented in tabCoordtabCoordWrapper

#' tabCoord
#'
#' @name tabCoord
#' @rdname tabCoord
#' @title Coordinate representation of compositional tables and a sample of compositional tables
#' @author Kamila Facevicova
#' @aliases tabCoord
#' @aliases tabCoordWrapper
#' @references Facevicova, K., Hron, K., Todorov, V. and M. Templ (2018)
#' General approach to coordinate representation of compositional tables.
#' Scandinavian Journal of Statistics, 45(4), 879--899.
#' @description tabCoord computes a system of orthonormal coordinates of a compositional table.
#' Computation of either pivot coordinates or a coordinate system based on the given SBP is possible.
#'
#' @param x a data frame containing variables representing row and column factors of the respective compositional table and variable with the values of the composition.
#' @param row.factor name of the variable representing the row factor. Needs to be stated with the quotation marks.
#' @param col.factor name of the variable representing the column factor. Needs to be stated with the quotation marks.
#' @param value name of the variable representing the values of the composition. Needs to be stated with the quotation marks.
#' @param SBPr an \eqn{I-1\times I} array defining the sequential binary partition of the values of the row factor, where I is the number of the row factor levels. The values assigned in the given step to the + group are marked by 1, values from  the - group by -1 and the rest by 0. If it is not provided, the pivot version of coordinates is constructed automatically.
#' @param SBPc an \eqn{J-1\times J} array defining the sequential binary partition of the values of the column factor, where J is the number of the column factor levels. The values assigned in the given step to the + group are marked by 1, values from  the - group by -1 and the rest by 0. If it is not provided, the pivot version of coordinates is constructed automatically.
#' @param pivot logical, default is FALSE. If TRUE, or one of the SBPs is not defined, its pivot version is used.
#' @param print.res logical, default is FALSE. If TRUE, the output is displayed in the Console.
#' @details This transformation moves the IJ-part compositional tables
#' from the simplex into a (IJ-1)-dimensional real space isometrically
#' with respect to its two-factorial nature.
#' The coordinate system is formed by two types of coordinates - balances and log odds-ratios.
#' @keywords multivariate
#' @export
#' @seealso
#' @return
#' \item{Coordinates}{an array of orthonormal coordinates.}
#' \item{Grap.rep}{graphical representation of the coordinates. Parts denoted by \verb{+} form the groups in the numerator of the respective computational formula, parts \verb{-} form the denominator and parts \verb{.} are not involved in the given coordinate.}
#' \item{Ind.coord}{an array of row and column balances. Coordinate representation of the independent part of the table.}
#' \item{Int.coord}{an array of OR coordinates. Coordinate representation of the interactive part of the table.}
#' \item{Contrast.matrix}{contrast matrix.}
#' \item{Log.ratios}{an array of pure log-ratios between groups of parts without the normalizing constant.}
#' \item{Coda.table}{table form of the given composition.}
#' \item{Bootstrap}{array of sample means, standard deviations and bootstrap confidence intervals.}
#' \item{Tables}{Table form of the given compositions.}
#' @examples
#' ###################
#' ### Coordinate representation of a CoDa Table
#'
#' # example from Fa\v cevicov\'a (2018):
#' data(manu_abs)
#' manu_USA <- manu_abs[which(manu_abs$country=='USA'),] #' manu_USA$output <- factor(manu_USA$output, levels=c('LAB', 'SUR', 'INP')) #' #' # pivot coordinates #' tabCoord(manu_USA, row.factor = 'output', col.factor = 'isic', value='value') #' #' # SBPs defined in paper #' r <- rbind(c(-1,-1,1), c(-1,1,0)) #' c <- rbind(c(-1,-1,-1,-1,1), c(-1,-1,-1,1,0), c(-1,-1,1,0,0), c(-1,1,0,0,0)) #' tabCoord(manu_USA, row.factor = 'output', col.factor = 'isic', value='value', SBPr=r, SBPc=c) tabCoord <- function(x=NULL, row.factor=NULL, col.factor=NULL, value=NULL, SBPr=NULL, SBPc=NULL, pivot=FALSE, print.res=FALSE){ # Control and subsidiary parameters setting if(is.null(row.factor)) stop('Name of the row factor is not defined!') if(is.null(col.factor)) stop('Name of the column factor is not defined!') if(is.null(value)) stop('Name of the value variable is not defined!') x[,row.factor] <- as.factor(x[,row.factor]) x[,col.factor] <- as.factor(x[,col.factor]) I <- nlevels(x[,row.factor]) # number of row factor levels J <- nlevels(x[,col.factor]) # number of column factor levels y <- x[,c(row.factor, col.factor, value)] x_vec <- y[order(y[,1], y[,2]),3] # vectorized table if(!identical(as.numeric(table(x[,row.factor])),as.numeric(rep(J,I)))) stop('The CoDa Table x is not defined properly, some values are missing!') if(!is.null(SBPr)&(nrow(SBPr)!= (I-1)||ncol(SBPr)!=I)) {warning('The row SBP is not defined properly, pivot coordinates are used!') SBPr <- NULL} if(!is.null(SBPc)&(nrow(SBPc)!= (J-1)||ncol(SBPc)!=J)) {warning('The column SBP is not defined properly, pivot coordinates are used!') SBPc <- NULL} ### Definition of pivot SBP (if necessary) if(is.null(SBPr)||pivot==TRUE) { SBPr <- numeric() for(j in 1:(I-1)) { novy <- c(rep(0,j-1), 1, rep(-1, I-j)) SBPr <- rbind(SBPr, novy) } } rownames(SBPr) <- NULL if(is.null(SBPc)||pivot==TRUE) { SBPc <- numeric() for(j in 1:(J-1)) { novy <- c(rep(0,j-1), 1, rep(-1, J-j)) SBPc <- rbind(SBPc, novy) } } rownames(SBPc) <- NULL log_kontrasty <- function(x){ r <- length(which(x==1)) s <- length(which(x==-1)) koef1 <- sqrt((r*s)/(r+s))*(1/r) koef2 <- -sqrt((r*s)/(r+s))*(1/s) log_kontrast <- rep(0, length(x)) log_kontrast[which(x==1)] <- koef1 log_kontrast[which(x==-1)] <- koef2 return(log_kontrast) } norm_const_balance <- function(x){ r <- length(which(x>0)) s <- length(which(x<0)) koef <- sqrt((r*s)/(r+s)) return(koef) } norm_const_OR <- function(x){ kladne = table(x[which(x>0)]) celkem = length(which(x!=0)) if(dim(kladne)==2) koef=sqrt((kladne[1]*kladne[2])/celkem) else koef=sqrt((kladne*kladne/4)/celkem) names(koef) = NULL return(koef) } ### expansion of SBP to the whole table SBPr_cele <- matrix(SBPr[,rep(c(1:I), rep(J,I))], ncol=I*J) SBPc_cele <- matrix(SBPc[,rep(c(1:J), I)], ncol=I*J) ### generating vectors of # balances LCr <- t(apply(SBPr_cele, 1, FUN=log_kontrasty)) LCc <- t(apply(SBPc_cele, 1, FUN=log_kontrasty)) # OR coordinates OR_deleni <- NULL for(i in 1:(I-1)) { for(j in 1:(J-1)) { novy <- LCr[i,]*LCc[j,] OR_deleni <- rbind(OR_deleni, novy) } } rownames(OR_deleni) <- NULL OR_contrasts <- t(apply(OR_deleni, 1, FUN=function(x){x/(norm(as.matrix(x), type="f"))})) ### matrix with generating vectors. Important for the back-transformation! contrasts <- rbind(LCr, LCc, OR_contrasts) ### Coordinates: coord.names <- c(paste('z', 1:(I-1), '^r', sep=''), paste('z', 1:(J-1), '^c', sep=''), paste('z', sort(outer(c(1:(I-1)),c(1:(J-1)), FUN=function(x,y)paste(x,y,sep=''))), '^OR', sep='')) souradnice <- contrasts%*%log(x_vec) rownames(souradnice) <- coord.names ind.coord <- souradnice[c(1:(I+J-2))] int.coord <- souradnice[c((I+J-1):(I*J-1))] names(ind.coord) <- coord.names[c(1:(I+J-2))] names(int.coord) <- coord.names[c((I+J-1):(I*J-1))] rownames(contrasts) <- coord.names colnames(contrasts) <- c(paste('x', sort(outer(c(1:I),c(1:J), FUN=function(x,y)paste(x,y,sep=''))), sep='')) ### Pure log-ratios between groups of parts (without normalizing constant) norm.constants.balance = apply(contrasts[1:(I+J-2), ], 1, norm_const_balance) norm.constants.OR = apply(contrasts[(I+J-1):(I*J-1), ], 1, norm_const_OR) norm.constants = c(norm.constants.balance, norm.constants.OR) log.ratios = souradnice/norm.constants ### Table form of the CoDa table tab <- spread(y, col.factor, value)[,-1] colnames(tab) <- levels(x[,col.factor]) rownames(tab) <- levels(x[,row.factor]) ### Graphical representation of groups within table: grap.rep <- array(NA, dim = c(I, J, I*J-1)) dimnames(grap.rep)[[1]] <- levels(x[,row.factor]) dimnames(grap.rep)[[2]] <- levels(x[,col.factor]) dimnames(grap.rep)[[3]] <- coord.names for(i in 1:nrow(contrasts)) { grap.r <- rep(".",ncol(contrasts)) grap.r[which(contrasts[i,]>0)] <- "+" grap.r[which(contrasts[i,]<0)] <- "-" grap.rep[,,i] <- t(matrix(grap.r, ncol=I)) } grap.rep <- noquote(grap.rep) ### Result if(print.res==TRUE) { print("Row balances:") print(souradnice[c(1:(I-1))]) print(grap.rep[c(1:(I-1))]) print("Column balances:") print(souradnice[c(I:(I+J-2))]) print(grap.rep[c(I:(I+J-2))]) print("Odds ratio coordinates:") print(souradnice[c((I+J-1):(I*J-1))]) print(grap.rep[c((I+J-1):(I*J-1))]) } result <- list("Coordinates"=souradnice, "Grap.rep" = grap.rep, "Ind.coord"=ind.coord, "Int.coord"=int.coord, "Contrast.matrix"=contrasts, 'Log.ratios'=log.ratios, 'Coda.table'=tab) return(result) } #' @rdname tabCoord #' @description tabCoordWrapper: For each compositional table in the sample \code{tabCoordWrapper} #' computes a system of orthonormal coordinates and provide a simple descriptive analysis. #' Computation of either pivot coordinates or a coordinate system based on the given SBP is possible. #' @details tabCoordWrapper: Each of n IJ-part compositional tables from the sample is with #' respect to its two-factorial nature isometrically transformed from the simplex #' into a (IJ-1)-dimensional real space. Sample mean values and standard deviations are #' computed and using bootstrap an estimate of 95 \% confidence interval is given. #' @param X a data frame containing variables representing row and column factors of the respective compositional tables, variable with the values #' of the composition and variable distinguishing the observations. #' @param obs.ID name of the variable distinguishing the observations. Needs to be stated with the quotation marks. #' @param test logical, default is \code{FALSE}. If \code{TRUE}, the bootstrap analysis of coordinates is provided. #' @param n.boot number of bootstrap samples. #' @export #' @examples #' #' ################### #' ### Analysis of a sample of CoDa Tables #' #' # example from Fa\v cevicov\'a (2018): #' data(manu_abs) #' #' ### Compositional tables approach, #' ### analysis of the relative structure. #' ### An example from Facevi\v cov\'a (2018) #' #' manu_abs$output <- factor(manu_abs$output, levels=c('LAB', 'SUR', 'INP')) #' #' # pivot coordinates #' tabCoordWrapper(manu_abs, obs.ID='country', #' row.factor = 'output', col.factor = 'isic', value='value') #' #' # SBPs defined in paper #' r <- rbind(c(-1,-1,1), c(-1,1,0)) #' c <- rbind(c(-1,-1,-1,-1,1), c(-1,-1,-1,1,0), #' c(-1,-1,1,0,0), c(-1,1,0,0,0)) #' tabCoordWrapper(manu_abs, obs.ID='country',row.factor = 'output', #' col.factor = 'isic', value='value', SBPr=r, SBPc=c, test=TRUE) #' #' ### Classical approach, #' ### generalized linear mixed effect model. #' #' \dontrun{ #' library(lme4) #' glmer(value~output*as.factor(isic)+(1|country),data=manu_abs,family=poisson) #' } tabCoordWrapper <- function(X, obs.ID=NULL, row.factor=NULL, col.factor=NULL, value=NULL, SBPr=NULL, SBPc=NULL, pivot=FALSE, test=FALSE, n.boot=1000) { # Control and subsidiary parameters setting if(is.null(obs.ID)) stop('Name of the observation ID variable is not defined!') if(is.null(row.factor)) stop('Name of the row factor is not defined!') if(is.null(col.factor)) stop('Name of the column factor is not defined!') if(is.null(value)) stop('Name of the value variable is not defined!') X[,obs.ID] <- as.factor(X[,obs.ID]) X[,row.factor] <- as.factor(X[,row.factor]) X[,col.factor] <- as.factor(X[,col.factor]) N <- nlevels(X[,obs.ID]) I <- nlevels(X[,row.factor]) # number of row factor levels J <- nlevels(X[,col.factor]) # number of column factor levels if(!identical(as.numeric(table(X[,c(row.factor,obs.ID)])),as.numeric(rep(J,(I*N))))) stop('The CoDa Tables are not defined properly, some values are missing!') if(!is.null(SBPr)&(nrow(SBPr)!= (I-1)||ncol(SBPr)!=I)) {warning('The row SBP is not defined properly, pivot coordinates are used!') SBPr <- NULL} if(!is.null(SBPc)&(nrow(SBPc)!= (J-1)||ncol(SBPc)!=J)) {warning('The column SBP is not defined properly, pivot coordinates are used!') SBPc <- NULL} Coordinates <- NULL Log.ratios <- NULL Ind.coord <- NULL Int.coord <- NULL Tables <- array(NA, c(I, J, N)) for(i in 1:N) { obs <- which(X[,obs.ID]==levels(X[,obs.ID])[i]) new <- tabCoord(x=X[obs,], row.factor=row.factor, col.factor=col.factor, value=value, SBPr=SBPr, SBPc=SBPc, pivot=pivot, print.res=FALSE) Coordinates <- cbind(Coordinates, new$Coordinates)
Log.ratios <- cbind(Log.ratios, new$Log.ratios) Ind.coord <- cbind(Ind.coord, new$Ind.coord)
Int.coord <- cbind(Int.coord, new$Int.coord) Tables[,,i] <- as.matrix(new$Coda.table)

}
Coordinates <- t(Coordinates)

rownames(Coordinates) <- levels(X[,obs.ID])
colnames(Log.ratios) <- levels(X[,obs.ID])
colnames(Ind.coord) <- levels(X[,obs.ID])
colnames(Int.coord) <- levels(X[,obs.ID])
dimnames(Tables)[[1]] <- levels(X[,row.factor])
dimnames(Tables)[[2]] <- levels(X[,col.factor])
dimnames(Tables)[[3]] <- levels(X[,obs.ID])

res <- list('Coordinates'=(Coordinates), 'Log.ratios'=t(Log.ratios), 'Ind.coord'=t(Ind.coord), 'Int.coord'=t(Int.coord),
'Grap.rep'=new$Grap.rep, 'Contrast.matrix'=new$Contrast.matrix, 'Tables'=Tables)

if(test==TRUE)
{
# sample characteristics
mean <- apply(Coordinates, 2, mean)
sd <- apply(Coordinates, 2, sd)

#set.seed(123)

# pocet opakovani bootstrapu
opakovani <- n.boot
I <- nlevels(X[,row.factor])
J <- nlevels(X[,col.factor])
xlab <- c(paste('z', 1:(I-1), '^r', sep=''), paste('z', 1:(J-1), '^c', sep=''), paste('z', sort(outer(c(1:(I-1)),c(1:(J-1)), FUN=function(x,y)paste(x,y,sep=''))), '^OR', sep=''))
boxplot(Coordinates, notch=TRUE, names=xlab)
abline(a=0, b=0, lty="dashed")

means <- t(replicate(opakovani,apply(Coordinates[sample(N,replace=TRUE),],2,mean)))
CIl <- apply(means,2,quantile,0.025)
CIu <- apply(means,2,quantile,0.975)
Bootstrap <- cbind(mean, sd, CIl, CIu)

res <- list('Coordinates'=(Coordinates), 'Log.ratios'=t(Log.ratios), 'Ind.coord'=t(Ind.coord), 'Int.coord'=t(Int.coord),
'Grap.rep'=new$Grap.rep, 'Contrast.matrix'=new$Contrasts, 'Bootstrap'=Bootstrap, 'Tables'=Tables)
}

return(res)
}


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robCompositions documentation built on Jan. 13, 2021, 10:07 p.m.