plotlmrdia: Plot L-moment Ratio Diagram
In lmomco: L-Moments, Censored L-Moments, Trimmed L-Moments, L-Comoments, and Many Distributions

plotlmrdia

R Documentation

Plot L-moment Ratio Diagram

Description

Plot the L-moment ratio diagram of L-skew and L-kurtosis from an L-moment ratio diagram object returned by lmrdia. This diagram is useful for selecting a distribution to model the data. The application of L-moment diagrams is well documented in the literature. This function is intended to function as a demonstration of L-moment ratio diagram plotting.

Usage

plotlmrdia(lmr=NULL, nopoints=FALSE, nolines=FALSE, nolimits=FALSE,
           noaep4=FALSE, nogev=FALSE, noglo=FALSE, nogpa=FALSE, nope3=FALSE,
           nopdq3=FALSE, nogno=FALSE, nogov=FALSE, nocau=TRUE,  noexp=FALSE,
           nonor=FALSE,  nogum=FALSE, noray=FALSE, nosla=TRUE,  nouni=FALSE,
           xlab="L-SKEW", ylab="L-KURTOSIS", add=FALSE, empty=FALSE,
           autolegend=FALSE, xleg=NULL, yleg=NULL, legendcex=0.9,
           ncol=1, text.width=NULL, ...)

Arguments

`lmr`	L-moment diagram object from `lmrdia`, if `NULL`, then `empty` is internally set to `TRUE`.
`nopoints`	If `TRUE` then point distributions are not drawn.
`nolines`	If `TRUE` then line distributions are not drawn.
`nolimits`	If `TRUE` then theoretical limits of L-moments are not drawn.
`noaep4`	If `TRUE` then the lower bounds line of Asymmetric Exponential Power distribution is not drawn.
`nogev`	If `TRUE` then line of Generalized Extreme Value distribution is not drawn.
`noglo`	If `TRUE` then line of Generalized Logistic distribution is not drawn.
`nogno`	If `TRUE` then line of Generalized Normal (Log-Normal3) distribution is not drawn.
`nogov`	If `TRUE` then line of Govindarajulu distribution is not drawn.
`nogpa`	If `TRUE` then line of Generalized Pareto distribution is not drawn.
`nope3`	If `TRUE` then line of Pearson Type III distribution is not drawn.
`nopdq3`	If `TRUE` then line of Polynomial Density-Quantile3 distribution is not drawn.
`nocau`	If `TRUE` then point (limiting, TL-moment [trim=1]) of the Cauchy distribution is not drawn.
`noexp`	If `TRUE` then point of Exponential distribution is not drawn.
`nonor`	If `TRUE` then point of Normal distribution is not drawn.
`nogum`	If `TRUE` then point of Gumbel distribution is not drawn.
`noray`	If `TRUE` then point of Rayleigh distribution is not drawn.
`nouni`	If `TRUE` then point of Uniform distribution is not drawn.
`nosla`	If `TRUE` then point (limiting, TL-moment [trim=1]) of the Slash distribution is not drawn.
`xlab`	Horizonal axis label passed to `xlab` of the `plot` function.
`ylab`	Vertical axis label passed to `ylab` of the `plot` function.
`add`	A logical to toggle a call to `plot` to start a new plot, otherwise, just the trajectories are otherwise plotted.
`empty`	A logical to return before any trajectories are plotted but after the condition of the `add` has been evaluated, and “empty” character string is returned.
`autolegend`	Generate the legend by built-in algorithm.
`xleg`	X-coordinate of the legend. This argument is checked for being a character versus a numeric. If it is a character, then `yleg` is not needed and `xleg` and take on “location may also be specified by setting x to a single keyword” as per the functionality of `graphics::legend()` itself.
`yleg`	Y-coordinate of the legend.
`legendcex`	The `cex` to pass to `graphics::legend()`.
`ncol`	The number of columns in which to set the legend items (default is 1, which differs from `legend()` default of 1).
`text.width`	Argument of the same name for `legend`. Setting to 0.1 for `ncol` set to 2 seems to work pretty well when two columns are desired.
`...`	Additional arguments passed onto the `plot` function.

Note

This function provides hardwired calls to lines and points to produce the diagram. The plot symbology for the shown distributions is summarized here. The Asymmetric Exponential Power and Kappa (four parameter) and Wakeby (five parameter) distributions are not well represented on the diagram as each constitute an area (Kappa) or hyperplane (Wakeby) and not a line (3-parameter distributions) or a point (2-parameter distributions). However, the Kappa demarks the area bounded by the Generalized Logistic (glo) on the top and the theoretical L-moment limits on the bottom. The Asymmetric Exponential Power demarks its own unique lower boundary and extends up in the \tau_4 direction to \tau_4 = 1. However, parameter estimation with L-moments has lost considerable accuracy for \tau_4 that large (see Asquith, 2014).

GRAPHIC TYPE	GRAPHIC NATURE
L-moment Limits	line width 2 and color 8 (grey)
Asymmetric Exponential Power (4-p)	line width 1, line type 4 (dot), and color red
Generalized Extreme Value	line width 1, line type 2 (dash), and color red
Generalized Logistic	line width 1 and color green
Generalized Normal	line width 1, line type 2 (dash), and color blue
Govindarajulu	line width 1, line type 2 (dash), and color 6 (purple)
Generalized Pareto	line width 1 and color blue
Pearson Type III	line width 1 and color 6 (purple)
Polynomial Density-Quantile3	line width 1.3, line type 2 (dash), and color darkgreen
Exponential	symbol 16 (filled circle) and color red
Normal	symbol 15 (filled square) and color red
Gumbel	symbol 17 (filled triangle) and color red)
Rayleigh	symbol 18 (filled diamond) and color red
Uniform	symbol 12 (square and a plus sign) and color red
Cauchy	symbol 13 (circle with over lapping `\times`) and color green
Slash	symbol 10 (cicle containing `+`) and color green

Author(s)

W.H. Asquith

References

Asquith, W.H., 2011, Distributional analysis with L-moment statistics using the R environment for statistical computing: Createspace Independent Publishing Platform, ISBN 978–146350841–8.

Asquith, W.H., 2014, Parameter estimation for the 4-parameter asymmetric exponential power distribution by the method of L-moments using R: Computational Statistics and Data Analysis, v. 71, pp. 955–970.

Hosking, J.R.M., 1986, The theory of probability weighted moments: Research Report RC12210, IBM Research Division, Yorkton Heights, N.Y.

Hosking, J.R.M., 1990, L-moments—Analysis and estimation of distributions using linear combinations of order statistics: Journal of the Royal Statistical Society, Series B, v. 52, pp. 105–124.

Hosking, J.R.M., and Wallis, J.R., 1997, Regional frequency analysis–An approach based on L-moments: Cambridge University Press.

Vogel, R.M., and Fennessey, N.M., 1993, L moment diagrams should replace product moment diagrams: Water Resources Research, v. 29, no. 6, pp. 1745–1752.

Examples

plotlmrdia(lmrdia()) # simplest of all uses

## Not run: 
# A more complex example follows.
# For a given mean, L-scale, L-skew, and L-kurtosis, let us use a sample size
# of 30 and using 500 simulations, set the L-moments in lmr and fit the Kappa.
T3 <- 0.34; T4 <- 0.21; n <- 30; nsim <- 500
lmr <- vec2lmom(c(10000, 7500, T3, T4)); kap <- parkap(lmr)

# Next, create vectors for storing simulated L-skew (t3) and L-kurtosis (t4)
t3 <- t4 <- vector(mode = "numeric")

# Next, perform nsim simulations by randomly drawing from the Kappa distribution
# and compute the L-moments in sim.lmr and store the t3 and t4 of each sample.
for(i in 1:nsim) {
  sim.lmr <- lmoms(rlmomco(n,kap))
  t3[i] <- sim.lmr$ratios[3]; t4[i] <- sim.lmr$ratios[4]
}

# Next, plot the diagram with a legend at a specified location, and "zoom"
# into the diagram by manually setting the axis limits.
plotlmrdia(lmrdia(), autolegend=TRUE, xleg=0.1, yleg=0.41,
        xlim=c(-0.1, 0.5), ylim=c(-0.1, 0.4), nopoints=TRUE, empty=TRUE)

# Follow up by plotting the {t3, t4} values and the mean of these.
points(t3, t4)
points(mean(t3), mean(t4), pch=16, cex=3)

# Now plot the trajectories of the distributions.
plotlmrdia(lmrdia(), add=TRUE)

# Finally, plot crossing dashed lines at true values of L-skew and L-kurtosis.
lines(c(T3, T3), c(-1,  1), col=8, lty=2)
lines(c(-1, 1 ), c(T4, T4), col=8, lty=2) #
## End(Not run)

lmomco documentation built on Aug. 30, 2023, 5:10 p.m.