Res: Residuals from a linear regression

In Pstat: Assessing Pst Statistics

Description

'Res' calculates residuals from simple linear regressions (in particular to eliminate any variation resulting from allometric growth). These regression adjustments assume the existence of linear relationships between the dependent variables and the regressor (one of the column of the data frame).

Usage

Res(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.

Examples

# data(test)
# names(test)[9]
# Res(test,r=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)
    }
    Res.va <- function(dat, clm, re) {
        dat = dat[is.finite(dat[, re]), ]
        mea.clm = mean(dat[is.finite(dat[, clm]), clm], na.rm = TRUE)
        mea.reg = mean(dat[is.finite(dat[, clm]), re], na.rm = TRUE)
        a = sum((dat[is.finite(dat[, clm]), re] - mea.reg) * 
            dat[is.finite(dat[, clm]), clm])/sum((dat[is.finite(dat[, 
            clm]), re] - mea.reg) * (dat[is.finite(dat[, clm]), 
            re] - mea.reg))
        b = mea.clm - a * mea.reg
        dat[, clm] = dat[, clm] - b - a * dat[, re]
        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 = Res.va(data, clm = i + 1, 
            re = 2)
    else {
        for (i in 2:reg) data = Res.va(data, clm = i, re = reg + 
            1)
        if (reg < nb.var) 
            for (j in (reg + 1):nb.var) data = Res.va(data, clm = j + 
                1, re = reg + 1)
    }
    return(data[-(reg + 1)])
  }

Pstat documentation built on May 2, 2019, 5:56 a.m.