AitTrans {Pstat}

Aitchison transformation

Description

'AitTrans' calculates log-ratio transformed measurements to account for individual size-effects.

Usage

AitTrans(data, Rp = 0, Ri = 0)

Arguments

data

a dataframe with as many rows as individuals. The first column contains the name of the population to which the individual belongs, the others contain quantitative variables.

Rp

a vector containing the names of the populations to be deleted.

Ri

a vector containing each number of individual to be deleted. The vector Ri must contain existent individuals, each of them once.

Value

the data frame with transformed variables.

Author(s)

Blondeau Da Silva Stephane - Da Silva Anne.

References

Aitchison J., 1986. The Statistical Analysis of Compositional Data. Chapman and Hall, London - New York, XII, 416 pp.

Peres-Neto P.R., Magnan P., 2004. The influence of swimming demand on phenotypic plasticity and morphological integration: a comparison of two polymorphic charr species. Oecologia 140, 36-45.

Shinn C. et al., 2015. Phenotypic variation as an indicator of pesticide stress in gudgeon: accounting for confounding factors in the wild. Science of the Total Environment 538, 733-742.

Examples

data(test)
AitTrans(test)

## The function is currently defined as
function (data, Rp = 0, Ri = 0) 
{
    nb.var = dim(data)[2] - 1
    dat.rem.ind.pop <- function(data, ind = 0, pop = 0) {
        data = as.data.frame(data)
        dat.rem.ind <- function(dat, ind) {
            nb.rem.ind = length(ind)
            nb.ind = dim(dat)[1]
            for (i in 1:nb.rem.ind) dat = dat[row.names(dat)[1:(nb.ind - 
                i + 1)] != ind[i], ]
            return(dat)
        }
        dat.rem.pop <- function(dat, pop) {
            nb.rem.pop = length(pop)
            for (i in 1:nb.rem.pop) dat = dat[dat[, 1] != pop[i], 
                ]
            return(dat)
        }
        if (ind[1] != 0) 
            data = dat.rem.ind(data, ind)
        if (pop[1] != 0) 
            data = dat.rem.pop(data, pop)
        return(data)
    }
    nonNa.row <- function(data, row) {
        nb.na = 0
        for (i in 1:nb.var) if (is.na(data[row, i + 1])) 
            nb.na = nb.na + 1
        return(nb.var - nb.na)
    }
    data = dat.rem.ind.pop(data, ind = Ri, pop = Rp)
    nb.ind = dim(data)[1]
    cor.val = rep(0, nb.ind)
    for (i in 1:nb.ind) {
        cor.val[i] = 1/(nonNa.row(data, i)) * sum(log(data[i, 
            -1], base = 10), na.rm = TRUE)
        for (j in 1:nb.var) data[i, (j + 1)] = log(data[i, (j + 
            1)], base = 10) - cor.val[i]
    }
    return(data)
  }