robust: Algorithm that implements the robust method for reference...

Description Usage Arguments Value Author(s) References Examples

Description

The robust method is an iterative method that determines the most appropriate weighted mean of the data and then calculates the desired reference interval.

Usage

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robust(data, indices = c(1:length(data)), refConf = 0.95)

Arguments

data

Vector of data.

indices

Indices of data to use for calculations.

refConf

Desired coverage of the reference interval. Default is 95 interval.

Value

Returns a vector containing the lower and upper limits of the reference interval.

Author(s)

Daniel Finnegan

References

Defining, Establishing, and Verifying Reference Intervals in the Clinical Laboratory; Approved Guideline - 3rd Edition (C28-A3)

Examples

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robust(set50)
robust(horn.outliers(set20)$subset)

## The function is currently defined as
function (data, indices = c(1:length(data)), refConf = 0.95) 
{
    data = sort(data[indices])
    n = length(data)
    median = summary(data)[[3]]
    Tbi = median
    TbiNew = 10000
    c = 3.7
    MAD = summary(abs(data - median))[[3]]
    MAD = MAD/0.6745
    smallDiff = FALSE
    repeat {
        ui = (data - Tbi)/(c * MAD)
        ui[ui < -1] = 1
        ui[ui > 1] = 1
        wi = (1 - ui^2)^2
        TbiNew = (sum(data * wi)/sum(wi))
        if ((abs(TbiNew - Tbi)) < 1e-06) {
            break
        }
        Tbi = TbiNew
    }
    ui = NULL
    ui = (data - median)/(205.6 * MAD)
    sbi205.6 = 205.6 * MAD * sqrt((n * sum(((1 - ui[ui > -1 & 
        ui < 1]^2)^4) * ui[ui > -1 & ui < 1]^2))/(sum((1 - ui[ui > 
        -1 & ui < 1]^2) * (1 - 5 * ui[ui > -1 & ui < 1]^2)) * 
        max(c(1, -1 + sum((1 - ui[ui > -1 & ui < 1]^2) * (1 - 
            5 * ui[ui > -1 & ui < 1]^2))))))
    ui = NULL
    ui = (data - median)/(3.7 * MAD)
    sbi3.7 = 3.7 * MAD * sqrt((n * sum(((1 - ui[ui > -1 & ui < 
        1]^2)^4) * ui[ui > -1 & ui < 1]^2))/(sum((1 - ui[ui > 
        -1 & ui < 1]^2) * (1 - 5 * ui[ui > -1 & ui < 1]^2)) * 
        max(c(1, -1 + sum((1 - ui[ui > -1 & ui < 1]^2) * (1 - 
            5 * ui[ui > -1 & ui < 1]^2))))))
    ui = NULL
    ui = (data - Tbi)/(3.7 * sbi3.7)
    St3.7 = 3.7 * sbi3.7 * sqrt((sum(((1 - ui[ui > -1 & ui < 
        1]^2)^4) * ui[ui > -1 & ui < 1]^2))/(sum((1 - ui[ui > 
        -1 & ui < 1]^2) * (1 - 5 * ui[ui > -1 & ui < 1]^2)) * 
        max(c(1, -1 + sum((1 - ui[ui > -1 & ui < 1]^2) * (1 - 
            5 * ui[ui > -1 & ui < 1]^2))))))
    tStatistic = qt(1 - ((1 - refConf)/2), (n - 1))
    margin = tStatistic * sqrt(sbi205.6^2 + St3.7^2)
    robustLower = Tbi - margin
    robustUpper = Tbi + margin
    RefInterval = c(robustLower, robustUpper)
    return(RefInterval)
  }


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