copBasic (version 1.5.1)

lcomoms2.ABKGcop2parameter: Convert L-comoments to Parameters of Compositions of Two 2-parameter Copulas

Description

This function converts the L-comoments of a bivariate sample to the four parameters of a composition of two one-parameter copulas. Critical inputs are of course the first three dimensionless L-comoments: L-correlation, L-coskew, and L-cokurtosis. The must complex input is the solutionenvir, which is an environment containing arbitrarily long, but individual tables, of L-comoment and parameter pairings. These pairings could be computed from the examples in simcompositeCOP. The individual tables are scanned for potentially acceptable solutions and the absolute additive error of both L-comoments for a given order is controlled by the tNeps arguments. The default values seem acceptable. The purpose of the prescanning is to reduce the computation space from perhaps millions of solutions to a few orders of magnitude. The computation of the solution error can be further controlled by X or u with respect to Y or v using the comptNerrXY arguments, but experiments thus far indicate that the defaults are likely the most desired. A solution matching the L-correlation is always sought; thus there is no uset2err argument. The arguments uset3err and uset4err provide some level of granular control on addition error minimization; the defaults seek to match L-coskew and ignore L-cokurtosis. The setreturn controls which rank of computed solution is returned; users might want to manually inspect a few of the most favorable solutions, which can be done by the setreturn or inspection of the returned object from the lcomoms2.ABKGcop2parameter function. The examples are detailed and self contained to the copBasic package; users are asked to test these.

Usage

lcomoms2.ABKGcop2parameter(solutionenvir=NULL,
                           T2.12=NULL, T2.21=NULL,
                           T3.12=NULL, T3.21=NULL,
                           T4.12=NULL, T4.21=NULL,
                           t2eps=0.1, t3eps=0.1, t4eps=0.1,
                           compt2erruv=TRUE, compt2errvu=TRUE,
                           compt3erruv=TRUE, compt3errvu=TRUE,
                           compt4erruv=TRUE, compt4errvu=TRUE,
                           uset3err=TRUE, uset4err=FALSE,
                           setreturn=1, maxtokeep=1e5)

Arguments

solutionenvir

The environment containing solutions;

T2.12

L-correlation $\tau_2^{[12]}$;

T2.21

L-correlation $\tau_2^{[21]}$;

T3.12

L-coskew $\tau_3^{[12]}$;

T3.21

L-coskew $\tau_3^{[21]}$;

T4.12

L-cokurtosis $\tau_4^{[12]}$;

T4.21

L-cokurtosis $\tau_4^{[21]}$;

t2eps

An error term in which to pick a potential solution as close enough on preliminary processing for $\tau_2^{[1 \leftrightarrow 2]}$;

t3eps

An error term in which to pick a potential solution as close enough on preliminary processing for $\tau_3^{[1 \leftrightarrow 2]}$;

t4eps

An error term in which to pick a potential solution as close enough on preliminary processing for $\tau_4^{[1 \leftrightarrow 2]}$;

compt2erruv

Compute an L-correlation error using the 1 with respect to 2 (or $u$ wrt $v$);

compt2errvu

Compute an L-correlation error using the 2 with respect to 1 (or $v$ wrt $u$);

compt3erruv

Compute an L-coskew error using the 1 with respect to 2 (or $u$ wrt $v$);

compt3errvu

Compute an L-coskew error using the 2 with respect to 1 (or $v$ wrt $u$);

compt4erruv

Compute an L-cokurtosis error using the 1 with respect to 2 (or $u$ wrt $v$);

compt4errvu

Compute an L-cokurtosis error using the 2 with respect to 1 (or $v$ wrt $u$);

uset3err

Use the L-coskew error in the determination of the solution. The L-correlation error is always used;

uset4err

Use the L-cokurtosis error in the determination of the solution. The L-correlation error is always used;

setreturn

Set (index) number of the solution to return. The default of 1 returns the preferred solutions based on the controls for the minimization; and

maxtokeep

The value presets the number of rows in the solution matrix. This matrix is filled with potential solutions as the various subfiles of the solutionenvir are scanned. The matrix is trimmed of NAs and error trapping is in place for

Value

• A data frame is returned.

References

Asquith, W.H., 2011, Distributional analysis with L-moment statistics using the R environment for statistical computing: CreateSpace, ISBN 978-1-463508418, 344 p.

Salvadori, G., De Michele, C., Kottegoda, N.T., and Rosso, R., 2007, Extremes in Nature---An approach using copulas: Springer, 289 p.

See Also

simCOP, simcompositeCOP, composite3COP

Examples

data(PlackettPlackettABKGtest)  # grab a solution table for a
# Plackett-Plackett composited copula from the copBasic package
# Then create an environment to house the "table".
PlackettPlackettABKG <- new.env()
assign("NeedToCreateForDemo", PlackettPlackettABKGtest, envir=PlackettPlackettABKG)
# Now that the table is assigned into the environment, the parameter
# estimation function can be used. In reality a much much larger
# solution set is needed and possibility available from the author.

PPcop <- lcomoms2.ABKGcop2parameter(solutionenvir=PlackettPlackettABKG,
                                    T2.12=-0.5059, T2.21=-0.5110,
                                    T3.12= 0.15,   T3.21= 0.17,
                                    T4.12=-0.050,  T4.21= 0.0329,
                                    uset3err=TRUE, uset4err=TRUE)
para <- list(cop1=PLACKETTcop, cop2=PLACKETTcop,
             alpha=PPcop$alpha, beta=PPcop$beta,
             kappa=PPcop$kappa, gamma=PPcop$gamma,
             para1=PPcop$Cop1Thetas, para2=PPcop$Cop2Thetas)

D <- simCOP(n=1000, cop=composite3COP,
            para=para, col=rgb(0,0,0,0.1), pch=16)


T2.12 <- .7675;   T2.21 <- 0.7675
T3.12 <- -0.01096; T3.21 <- 0.01392
T4.12 <- 0.08920;  T4.21 <- 0.08123
PPcop <- lcomoms2.ABKGcop2parameter(solutionenvir=PlackettPlackettABKG,
                                    T2.12=T2.12, T2.21=T2.21,
                                    T3.12=T3.12, T3.21=T3.21,
                                    T4.12=T4.12, T4.21=T4.21, uset4err=TRUE)
para <- list(cop1=PLACKETTcop, cop2=PLACKETTcop,
             alpha=PPcop$alpha, beta=PPcop$beta,
             kappa=PPcop$kappa, gamma=PPcop$gamma,
             para1=PPcop$Cop1Thetas, para2=PPcop$Cop2Thetas)
D <- simCOP(n=1000, cop=composite3COP,
            para=para, col=rgb(0,0,0,0.1), pch=16)
level.curvesCOP(cop=composite3COP, para=para, delt=.1, ploton=FALSE)
qua.regressCOP.draw(cop=composite3COP, para=para,
                    ploton=FALSE, F=c(seq(0.05, 0.95, by=0.05)))
qua.regressCOP.draw(cop=composite3COP, para=para, swap=TRUE,
                    ploton=FALSE, F=c(seq(0.05, 0.95, by=0.05)),
                    col=c(3,2))
diag <- diagCOP(cop=composite3COP, para=para, ploton=FALSE, lwd=4)

# One can inspect alternative solutions like this
# S <- PPcop$solutions$solutions[,1:18]
# B <- S[abs(S$t2.12res) < 0.02 & abs(S$t2.21res) < 0.02 &
#         abs(S$t3.12res) < 0.02 & abs(S$t3.21res) < 0.02, ]
#print(B)