# theoTLmoms: The Theoretical Trimmed L-moments and TL-moment Ratios using... In lmomco: L-Moments, Censored L-Moments, Trimmed L-Moments, L-Comoments, and Many Distributions

 theoTLmoms R Documentation

## The Theoretical Trimmed L-moments and TL-moment Ratios using Integration of the Quantile Function

### Description

Compute the theoretrical trimmed L-moments (TL-moments) for a vector. The level of symmetrical or asymmetrical trimming is specified. A theoretrical TL-moment in integral form is

 \lambda^{(t_1,t_2)}_r = \underbrace{\frac{1}{r}}_{\stackrel{\mbox{average}}{\mbox{of terms}}} \sum^{r-1}_{k=0} \overbrace{(-1)^k}^{\mbox{differences}} \underbrace{ r-1 \choose k }_{\mbox{combinations}} \frac{\overbrace{(r+t_1+t_2)!}^{\mbox{sample size}}\: I^{(t_1,t_2)}_r} {\underbrace{(r+t_1-k-1)!}_{\mbox{left tail}} \underbrace{(t_2+k)!}_{\mbox{right tail}}} \mbox{, in which }

 I^{(t_1,t_2)}_r = \int^1_0 \underbrace{x(F)}_{\stackrel{\mbox{quantile}}{\mbox{function}}} \times \overbrace{F^{r+t_1-k-1}}^{\mbox{left tail}} \overbrace{(1-F)^{t_2+k}}^{\mbox{right tail}} \,\mathrm{d}F \mbox{,}

where x(F) is the quantile function of the random variable X for nonexceedance probability F, t_1 represents the trimming level of the t_1-smallest, t_2 represents the trimming level of the t_2-largest values, r represents the order of the L-moments. This function loops across the above equation for each nmom set in the argument list. The function x(F) is computed through the par2qua function. The distribution type is determined using the type attribute of the para argument—the parameter object.

As of version 1.5.2 of lmomco, there exists enhanced error trapping on integration failures in
theoTLmoms. The function now abandons operations should any of the integrations for the rth L-moment fail for reasons such as divergent integral or round off problems. The function returns NAs for all L-moments in lambdas and ratios.

### Usage

theoTLmoms(para, nmom=5, trim=NULL, leftrim=NULL, rightrim=NULL,
minF=0, maxF=1, quafunc=NULL,
nsim=50000, fold=5,
silent=TRUE, verbose=FALSE, ...)


### Arguments

 para A distribution parameter object of this package such as by vec2par. nmom The number of moments to compute. Default is 5. trim Level of symmetrical trimming to use in the computations. Although NULL in the argument list, the default is 0—the usual L-moment is returned. leftrim Level of trimming of the left-tail of the sample. rightrim Level of trimming of the right-tail of the sample. minF The end point of nonexceedance probability in which to perform the integration. Try setting to non-zero (but small) if you have a divergent integral. maxF The end point of nonexceedance probability in which to perform the integration. Try setting to non-unity (but close) if you have a divergent integral. quafunc An optional and arbitrary quantile function that simply needs to except a nonexceedance probability and the parameter object in para. This is a feature that permits computation of the L-moments of a quantile function that does not have to be implemented in the greater overhead hassles of the lmomco style. This feature might be useful for estimation of quantile function mixtures or those distributions not otherwise implemented in this package. nsim Simulation size for Monte Carlo integration is such is internally deemed necessary (see silent argument). fold The number of fractions or number of folds of nsim, which in other words, means that nsim is divided by folds and a loop creating folds integrations of nsim/folds is used from which the mean and mean absolute error of the integrand are computed. This is to try to recover similar output as integrate(). silent The argument of silent for the try() operation wrapped on integrate(). If set true and the integral is probability divergent, Monte Carlo integration is triggered using nsim and folds. The user would have to set verbose=TRUE to then acquire the returned table in integrations of the integration passes including those are or are not Monte Carlo. verbose Toggle verbose output. Because the R function integrate is used to perform the numerical integration, it might be useful to see selected messages regarding the numerical integration. ... Additional arguments to pass.

### Value

An R list is returned.

 lambdas Vector of the TL-moments. First element is \lambda^{(t_1,t_2)}_1, second element is \lambda^{(t_1,t_2)}_2, and so on. ratios Vector of the L-moment ratios. Second element is \tau^{(t_1,t_2)}, third element is \tau^{(t_1,t_2)}_3 and so on. trim Level of symmetrical trimming used in the computation, which will equal NULL if asymmetrical trimming was used. leftrim Level of left-tail trimming used in the computation. rightrim Level of right-tail trimming used in the computation. nsim Echo of the nsim argument if and only if at least one Monte Carlo integration was required, otherwise this is set to “not needed” on the return. folds Echo of the folds argument if and only if at least one Monte Carlo integration was required, otherwise this is set to “not needed” on the return. monte_carlo A logical vector of whether one or more Monte Carlo integrations was needed for the r-th index of the vector during the integrations for the r-th L-moment. source An attribute identifying the computational source of the L-moments: “theoTLmoms” or switched to “theoLmoms” if this function was dispatched from theoLmoms. integrations If verbose=TRUE, then the results of the integrations are a data frame stored here. Otherwise, integrations is not present in the list.

### Note

An extended example of a unique application of the TL-moments is useful to demonstrate capabilities of the lmomco package API. Consider the following example in which the analyst has 21 years of data for a given spatial location. Based on regional analysis, the highest value (the outlier = 21.12) is known to be exotically high but also documentable as not representing say a transcription error in the source database. The regional analysis also shows that the Generalized Extreme Value (GEV) distribution is appropriate.

The analyst is using a complex L-moment computational framework (say a software package called BigStudy.R) in which only the input data are under the control of the analyst or it is too risky to modify BigStudy.R. Yet, it is desired to somehow acquire robust estimation. The outlier value can be accommodated by estimating a pseudo-value and then simply make a substitution in the input data file for BigStudy.R.

The following code initiates pseudo-value estimation by storing the original 20 years of data in variable data.org and then extending these data with the outlier. The usual sample L-moments are computed in first.lmr and will only be used for qualitative comparison. A 3-dimensional optimizer will be used for the GEV so the starting point is stored in first.par.

  data.org  <- c(5.19, 2.58, 7.59, 3.22, 7.50, 4.05, 2.54, 9.00, 3.93, 5.15,
6.80, 2.10, 8.44, 6.11, 3.30, 5.75, 3.52, 3.48, 6.32, 4.07)
outlier   <- 21.12;            the.data  <- c(data.org, outlier)
first.lmr <- lmoms(the.data);  first.par <- pargev(first.lmr)


Robustness is acquired by computing the sample TL-moments such that the outlier is quantitatively removed by single trimming from the right side as the follow code shows:

  trimmed.lmr <- TLmoms(the.data, rightrim=1, leftrim=0)


The objective now is to fit a GEV to the sample TL-moments in trimmed.lmr. However, the right-trimmed only (t_1 = 0 and t_2 = 1) version of the TL-moments is being used and analytical solutions to the GEV for t = (0,1) are lacking or perhaps they are too much trouble to derive. The theoTLmoms function provides the avenue for progress because of its numerical integration basis for acquistion of the TL-moments. An objective function for the t_2 = 1 TL-moments of the GEV is defined and based on the sum of square errors of the first three TL-moments:

  "afunc" <- function(par, tarlmr=NULL, p=3) {
the.par  <- vec2par(par, type="gev", paracheck=FALSE)
fit.tlmr <- theoTLmoms(the.par, rightrim=1, leftrim=0)
return(sum((tarlmr$lambdas[1:p] - fit.tlmr$lambdas[1:p])^2))
}


and then optimize on this function and make a qualitative comparison between the original sample L-moments (untrimmed) to the equivalent L-moments (untrimmed) of the GEV having TL-moments equaling those in trimmed.lmr:

  rt <- optim(first.par$para, afunc, tarlmr=trimmed.lmr) last.lmr <- lmomgev(vec2par(rt$par, type="gev"))

message("# Original sample    L-moment lambdas: ",
paste(round(first.lmr$lambdas[1:3], digits=4), collapse=" ")) message("# Targeting back-fit L-moment lambdas: ", paste(round(last.lmr$lambdas[ 1:3], digits=4), collapse=" "))
# Original sample    L-moment lambdas: 5.7981 1.8565 0.7287
# Targeting back-fit L-moment lambdas: 5.5916 1.6501 0.5223


The primary result on comparison of the \lambda_r shows that the L-scale drops substantially as does L-skew: (\tau_3 = 0.7287 / 1.8565 = 0.3925 \rightarrow \lambda_3^{(t_2{=}1)} = 0.5223 / 1.6501 = 0.3165).

Now that the target L-moments (not TL-moments) are known (last.lmr), it is possible to optimize again on the value for the outlier that would provide the last.lmr within the greater computational framework in use by the analyst.

  "bfunc" <- function(x, tarlmr=NULL, p=3) {
sam.lmr <- lmoms(c(data.org, x))
return(sum((tarlmr$lambdas[1:p] - sam.lmr$lambdas[1:p])^2))
}
suppressWarnings(outlier.rt <- optim(outlier, bfunc, tarlmr=last.lmr))
# silence warning about 1D optimization with optim(), well behaved here

pseudo.outlier <- round(outlier.rt$par, digits=2) final.lmr <- lmoms(c(data.org, pseudo.outlier)) message("# Resulting new L-moment lambdas: ", paste(round(final.lmr$lambdas[1:3], digits=4), collapse=" "))
# Resulting new L-moment lambdas: 5.5914 1.6499 0.5221

message("# Pseudo-value for highest value: ", round(outlier.rt$par, digits=2)) # Pseudo-value for highest value: 16.78  Where the second optimization shows that if the largest value for the 21 years of data is given a value of 16.78 instead of its original value of 21.12 that the sample L-moments (untrimmed) will be consistent as if the TL-moments t = (0,1) has been somehow used without resorting to a risky re-coding of the greater computational framework. ### Author(s) W.H. Asquith ### References Elamir, E.A.H., and Seheult, A.H., 2003, Trimmed L-moments: Computational Statistics and Data Analysis, v. 43, pp. 299–314. ### See Also theoLmoms, TLmoms, tlmr2par ### Examples para <- vec2par(c(0, 1), type='nor') # standard normal TL00 <- theoTLmoms(para) # compute ordinary L-moments TL30 <- theoTLmoms(para, leftrim=3, rightrim=0) # trim 3 smallest samples # Let us look at the difference from simulation to theoretrical using # L-kurtosis and asymmetrical trimming for generalized Lambda dist. n <- 100 # really a much larger sample should be used---for speed P <- vec2par(c(10000, 10000, 6, 0.4),type='gld') Lkurt <- TLmoms(quagld(runif(n),P), rightrim=3, leftrim=0)$ratios[4]
theoLkurt <- theoTLmoms(P, rightrim=3, leftrim=0)$ratios[4] Lkurt - theoLkurt # as the number for runif goes up, this # difference goes to zero # Example using the Generalized Pareto Distribution # to verify computations from theoretical and sample stand point. n <- 100 # really a much larger sample should be used---for speed P <- vec2par(c(12, 34, 4),type='gpa') theoTL <- theoTLmoms(P, rightrim=2, leftrim=4) samTL <- TLmoms(quagpa(runif(n),P), rightrim=2, leftrim=4) del <- samTL$ratios[3] - theoTL$ratios[3] # if n is large difference # is small str(del) ## Not run: "cusquaf" <- function(f, para, ...) { # Gumbel-Normal product g <- vec2par(c(para[1:2]), type="gum") n <- vec2par(c(para[3:4]), type="nor") return(par2qua(f,g)*par2qua(f,n)) } para <- c(5.6, .45, 3, .3) theoTLmoms(para, quafunc=cusquaf) # L-skew = 0.13038711 ## End(Not run) ## Not run: # This example has a divergent integral triggered on the last of the inner # loop of the 4th L-moment call. Monte Carlo (MC) integration is thus triggered. # The verbose=TRUE saves numerical or MC integration result table to the return. para <- vec2par(c(2.00, 2.00, -0.20, -0.55), type="kap") lmrbck <- lmomkap( para, nmom=5) # print(lmrbck$lambdas) 3.1189568 1.9562688 0.4700229 0.4078741 0.1974055
lmrthe <- theoTLmoms2(para, nmom=5, verbose=TRUE)              # seed dependent
# print(lmrthe$lambdas) 3.1189569 1.9562686 0.4700227 0.4068539 0.1974049 parkap(lmrbck)$para # 2.00       2.00     -0.20      -0.55
parkap(lmrthe)\$para # 2.018883  1.986761  -0.202422  -0.570451 # seed dependent
## End(Not run)


lmomco documentation built on May 29, 2024, 10:06 a.m.