Cascades | R Documentation |
The data on annual precipitation totals for the North Cascades region contains the sample L-moments ratios (L-CV, L-skewness and L-kurtosis) for 19 sites as used by Hosking and Wallis (1997), page 53, Table 3.4, to illustrate screening tools for regional freqency analysis (RFA).
data(Cascades)
A data frame with 19 observations on the following 3 variables.
L-CV
L-coefficient of variation
L-skewness
L-coefficient of skewness
L-kurtosis
L-coefficient of kurtosis
The sample L-moment ratios (L-CV, L-skewness and L-kurtosis) of a site are regarded as a point in three dimensional space.
Hosking, J. R. M. and J. R. Wallis (1997), Regional Frequency Analysis: An Approach Based on L-moments. Cambridge University Press, p. 52–53
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
data(Cascades)
# plot a matrix of scatterplots
pairs(Cascades,
main="Cascades data set",
pch=21,
bg=c("red", "green3", "blue"))
mcd<-CovMcd(Cascades)
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
rd <- sqrt(getDistance(mcd))
ccov <- CovClassic(Cascades)
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")
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