lmom32: Hosking and Wallis Data Set, Table 3.2

In rrcov: Scalable Robust Estimators with High Breakdown Point

Hosking and Wallis Data Set, Table 3.2

Description

The data on annual maximum streamflow at 18 sites with smallest drainage area basin in southeastern USA contains the sample L-moments ratios (L-CV, L-skewness and L-kurtosis) as used by Hosking and Wallis (1997) to illustrate the discordancy measure in regional freqency analysis (RFA).

Usage

data(lmom32)

Format

A data frame with 18 observations on the following 3 variables.

L-CV

L-coefficient of variation

L-skewness

L-coefficient of skewness

L-kurtosis

L-coefficient of kurtosis

Details

The sample L-moment ratios (L-CV, L-skewness and L-kurtosis) of a site are regarded as a point in three dimensional space.

Source

Hosking, J. R. M. and J. R. Wallis (1997), Regional Frequency Analysis: An Approach Based on L-moments. Cambridge University Press, p.49, Table 3.2

References

Neykov, N.M., Neytchev, P.N., Van Gelder, P.H.A.J.M. and Todorov V. (2007), Robust detection of discordant sites in regional frequency analysis, Water Resources Research, 43, W06417, doi:10.1029/2006WR005322

Examples

    data(lmom32)

    # plot a matrix of scatterplots
    pairs(lmom32,
          main="Hosking and Wallis Data Set, Table 3.3",
          pch=21,
          bg=c("red", "green3", "blue"))

    mcd<-CovMcd(lmom32)
    mcd
    plot(mcd, which="dist", class=TRUE)
    plot(mcd, which="dd", class=TRUE)

    ##  identify the discordant sites using robust distances and compare 
    ##  to the classical ones
    mcd <- CovMcd(lmom32)
    rd <- sqrt(getDistance(mcd))
    ccov <- CovClassic(lmom32)
    cd <- sqrt(getDistance(ccov))
    r.out <- which(rd > sqrt(qchisq(0.975,3)))
    c.out <- which(cd > sqrt(qchisq(0.975,3)))
    cat("Robust: ", length(r.out), " outliers: ", r.out,"\n")
    cat("Classical: ", length(c.out), " outliers: ", c.out,"\n")

rrcov documentation built on July 9, 2023, 6:03 p.m.