overlapBiclustersByRows {Anthropometry}

Overlapped biclusters by rows

Description

This function allows us to check which rows belong to more than one bicluster. It is used within the CCbiclustAnthropo function.

Usage

overlapBiclustersByRows(Bic,resBicluster)

Arguments

Bic

Bicluster number.

resBicluster

An object of class Biclust.

Details

In order to know how this function works, it is necessary to understand the following commands:

res.bicl@RowxNumber[,1] indicates the rows that belong to the bicluster 1, by assigning a TRUE value to the position of those rows inside the original matrix. By using table(res.bicl@RowxNumber[,1]), we obtain the number of rows belonging to bicluster 1.

1 * res.bicl@RowxNumber[,1] makes TRUES into 1s.

Bic * res.bicl@RowxNumber[,Bic] makes TRUES into the corresponding value of Bic.

In short, this function puts a 1 in those rows belonging to bicluster 1, a 2 in those ones of bicluster 2, and so on.

The fact that certain columns of the matrix returned by this function have a value different from 0 at the same row, will indicate that that row belong to both biclusters.

This function cannot be used with the data of the package. This function is included in the package in the hope that it could be helpful or useful for other researchers.

Value

A matrix with as many rows as rows of the original matrix, and as many columns as obtained biclusters.

Author(s)

Guillermo Vinue

References

Vinue, G., and Ibanez, M. V., (2014), Data depth and Biclustering applied to anthropometric data. Exploring their utility in apparel design. Technical report.

Kaiser, S., and Leisch, F., (2008). A Toolbox for Bicluster Analysis in R. Tech.rep., Department of Statistics (University of Munich).

See Also

CCbiclustAnthropo

Examples

## Not run: 
#Note: package biclust needed.
#This is an example of using this function with a certain database 
#made up of body dimensions related to the lower body part.
data <- dataUser[(waist >= 58) & (waist < 115),] #dataUser is the user database.
rownames(data) <- 1:dim(data)[1]
  
waist <- data[,"WaistCircumference"] 
    
waist_4 <- seq(58, 86, 4) 
waist_6 <- seq(91, 115, 6) 
waistCirc <- c(waist_4,waist_6)
nsizes <- length(waistCirc) 

#Position of the body variables in the database:
lowerVars <- c(14, 17:25, 27, 28, 65:73, 75, 77:81, seq(100, 116, 2))

nBic <- c(2, 2, 4, rep(5, 7), 3, 3)  
diffRanges <- list(c(14,20), c(24,30), c(24,30), c(33,39), c(29,35), c(29,35), 
                   c(28,35), c(31,38), c(31,38), c(30,37), c(26,33), c(25,32))
percDisac <- 0.01 
dir <- "/home/guillermo/"
  
res_bicl_antropom <- CCbiclustAnthropo(data,waist,waistCirc,lowerVars,
                                       nsizes,nBic,diffRanges,percDisac,dir)

#For a single size:
size <- 5
res <- res_bicl_antropom[[1]][[size]]

sapply(1 : res@Number, overlapBiclustersByRows, res)

## End(Not run)