ReistTrans {Pstat}

Reist standardization

Description

'ReistTrans' calculates residuals (size adjusted measurements) from Reist tranformations to eliminate any variation resulting from allometric growth. There is a single regressor (one of the quantitative traits).

Usage

ReistTrans(data, reg, 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.

reg

the name (or the rank) of the variable chosen as the explanatory variable.

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 of adjusted variables, the column containing the quantitative trait used as a regressor being deleted.

Note

dispensable quantitative measures can easily be deleted in the main functions of R.

Author(s)

Blondeau Da Silva Stephane - Da Silva Anne.

References

Reist J.D., 1985. An empirical evaluation of several univariate methods that adjust for size variation in morphometric data. Canadian Journal Zoology 63, 1429-1439.

Kaeuffer R. et al., 2012. Parallel and nonparallel aspects of ecological, phenotypic, and genetic divergence across replicate population pairs of lake and stream stickleback. Evolution 66(2), 402-418.

He Y. et al., 2013. Morphological Variation Among Wild Populations of Chinese Rare Minnow (Gobiocypris rarus): Deciphering the Role of Evolutionary Processes. Zoological Science 30, 475-483.

Examples

data(test)
names(test)[9]
ReistTrans(test,reg=9)

## The function is currently defined as
function (data, reg, Rp = 0, Ri = 0) 
{
    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)
    }
    Reitra.va <- function(dat, clm, re) {
        dat = dat[is.finite(dat[, re]), ]
        log.dat = dat
        mea = mean(dat[is.finite(dat[, clm]), re])
        log.dat[, clm] = log(dat[, clm], base = 10)
        log.dat[, re] = log(dat[, re], base = 10)
        mea.clm = mean(log.dat[is.finite(log.dat[, clm]), clm], 
            na.rm = TRUE)
        mea.reg = mean(log.dat[is.finite(log.dat[, clm]), re], 
            na.rm = TRUE)
        a = sum((log.dat[is.finite(log.dat[, clm]), re] - mea.reg) * 
            log.dat[is.finite(log.dat[, clm]), clm])/sum((log.dat[is.finite(log.dat[, 
            clm]), re] - mea.reg) * (log.dat[is.finite(log.dat[, 
            clm]), re] - mea.reg))
        dat[, clm] = log.dat[, clm] - a * (log.dat[, re] - log(mea, 
            base = 10))
        return(dat)
    }
    nb.var = dim(data)[2] - 1
    for (i in 1:nb.var) {
        if (names(data)[i + 1] == reg) 
            reg = i
    }
    if (is.numeric(reg) == FALSE) 
        return("reg value does not exist!")
    data = dat.rem.ind.pop(data, ind = Ri, pop = Rp)
    if (reg == 1) 
        for (i in 2:nb.var) data = Reitra.va(data, clm = i + 
            1, re = 2)
    else {
        for (i in 2:reg) data = Reitra.va(data, clm = i, re = reg + 
            1)
        if (reg < nb.var) 
            for (j in (reg + 1):nb.var) data = Reitra.va(data, 
                clm = j + 1, re = reg + 1)
    }
    return(data[-(reg + 1)])
  }