K {copula} | R Documentation

## Kendall Distribution Function for Archimedean Copulas

### Description

The Kendall distribution of an Archimedean copula is defined by

K(u) = P(C(U[1], U[2], …, U[d]) <= u),

where u in [0,1], and the d-dimensional (U[1],U[2],…,U[d]) is distributed according to the copula C. Note that the random variable C(U[1],U[2], …, U[d]) is known as “probability integral transform”. Its distribution function K is equal to the identity if d = 1, but is non-trivial for d >= 2.

`Kn()` computes the empirical Kendall distribution function, `pK()` the distribution function (so K() itself), `qK()` the quantile function, `dK()` the density, and `rK()` random number generation from K() for an Archimedean copula.

### Usage

    Kn(u, x, method = c("GR", "GNZ")) # empirical Kendall distribution function
    dK(u, copula, d, n.MC = 0, log.p = FALSE) # density
    pK(u, copula, d, n.MC = 0, log.p = FALSE) # df
    qK(p, copula, d, n.MC = 0, log.p = FALSE, # quantile function
       method = c("default", "simple", "sort", "discrete", "monoH.FC"),
       u.grid, xtraChecks = FALSE, ...)
    rK(n, copula, d) # random number generation

### Arguments

`u` | evaluation point(s) (in [0,1]).
`x` | data (in the d-dimensional space) based on which the Kendall distribution function is estimated.
`copula` | `acopula` with specified parameter, or (currently for `rK` only) a `outer_nacopula`.
`d` | dimension (not used when `copula` is an `outer_nacopula`).
`n.MC` | `integer`, if positive, a Monte Carlo approach is applied with sample size equal to `n.MC` to evaluate the generator derivatives involved; otherwise (`n.MC = 0`) the exact formula is used based on the generator derivatives as found by Hofert et al. (2012).
`log.p` | `logical`; if `TRUE`, probabilities p are given as log(p).
`p` | probabilities or log-probabilities if `log.p` is true.

`method`

for `qK()`, character string for the method how to compute the quantile function of K; available are:

"default"

  default method. Currently chooses `method="monoH.FC"` with `u.grid = 0:128/128`. This is fast but not too accurate (see example).

"simple"

  straightforward root finding based on `uniroot`.

"sort"

  root finding based on `uniroot` but first sorting `u`.

"discrete"

  first, K is evaluated at the given grid points `u.grid` (which should contain 0 and 1). Based on these probabilities, quantiles are computed with `findInterval`.

"monoH.FC"

  first, K is evaluated at the given grid points `u.grid`. A monotone spline is then used to approximate K. Based on this approximation, quantiles are computed with `uniroot`.

For `Kn()`, character string indicating the method according to which the empirical Kendall distribution is computed; available are:

"GR"

  the default. Computed as in Genest and Rivest (1993, Equations (4) and (5)).

"GNZ"

  computed as in Genest et al. (2011, Equation (19) and Lemma 1); this is guaranteed to satisfy that the estimator lies above the diagonal at any point in [0,1).

`u.grid` | (for `method="discrete"`:) The grid on which K is evaluated, a `numeric` vector.
`xtraChecks` | experimental logical indicating if extra checks should be done before calling `uniroot()` in some cases.
`...` | additional arguments passed to `uniroot` (for `method="default"`, `method="simple"`, `method="sort"`, and `method="monoH.FC"`) or `findInterval` (for `method="discrete"`), notably `tol` (`uniroot`) for increased accuracy.
`n` | sample size for `rK`.

### Details

For a completely monotone Archimedean generator psi,

K(u)=sum(k=0,...,d-1) psi^{(k)}(psi^{-1}(u))/k! (-psi^{-1}(u))^k, u in [0,1];

see Barbe et al. (1996). The corresponding density is

(-1)^d psi^{(d)}(psi^{-1}(u))(-(psi^{-1})'(u))(psi^{-1}(u))^{d-1}/(d-1)!

### Value

The empirical Kendall distribution function, density, distribution function, quantile function and random number generator.

### Note

Currently, the `"default"` method of `qK()` is fast but not very accurate, see the ‘Examples’ for more accuracy (with more CPU effort).

### References

Barbe, P., Genest, C., Ghoudi, K., and Rémillard, B. (1996), On Kendall's Process, Journal of Multivariate Analysis 58, 197–229.

Hofert, M., Mächler, M., and McNeil, A. J. (2012). Likelihood inference for Archimedean copulas in high dimensions under known margins. Journal of Multivariate Analysis 110, 133–150.

Genest, C. and Rivest, L.-P. (1993). Statistical inference procedures for bivariate Archimedean copulas. Journal of the American Statistical Association 88, 1034–1043.

Genest, C., G. Nešlehová, J., and Ziegel, J. (2011). Inference in multivariate Archimedean copula models. TEST 20, 223–256.

### See Also

`htrafo` or `emde` (where `K` is used); `splinefun(*, "monoHC")` for that method.

### Examples

    tau <- 0.5
    (theta <- copGumbel@iTau(tau)) # 2
    d <- 20
    (cop <- onacopulaL("Gumbel", list(theta,1:d)))

    ## Basic check of the empirical Kendall distribution function
    set.seed(271)
    n <- 1000
    U <- rCopula(n, copula = cop)
    X <- qnorm(U)
    K.sample <- pCopula(U, copula = cop)
    u <- seq(0, 1, length.out = 256)
    edfK <- ecdf(K.sample)
    plot(u, edfK(u), type = "l", ylim = 0:1,
         xlab = quote(italic(u)), ylab = quote(K[n](italic(u)))) # simulated
    K.n <- Kn(u, x = X)
    lines(u, K.n, col = "royalblue3") # Kn
    ## Difference at 0
    edfK(0) # edf of K at 0
    K.n[1] # K_n(0); this is > 0 since K.n is the edf of a discrete distribution
    ## => therefore, Kn(K.sample, x = X) is not uniform
    plot(Kn(K.sample, x = X), ylim = 0:1)
    ## Note: Kn(0) -> 0 for n -> Inf

    ## Compute Kendall distribution function
    u <- seq(0,1, length.out = 255)
    Ku    <- pK(u, copula = cop@copula, d = d) # exact
    Ku.MC <- pK(u, copula = cop@copula, d = d, n.MC = 1000) # via Monte Carlo
    stopifnot(all.equal(log(Ku),
    		    pK(u, copula = cop@copula, d = d, log.p=TRUE)))# rel.err 3.2e-16

    ## Build sample from K
    set.seed(1)
    n <- 200
    W <- rK(n, copula = cop)

    ## Plot empirical distribution function based on W
    ## and the corresponding theoretical Kendall distribution function
    ## (exact and via Monte Carlo)
    plot(ecdf(W), col = "blue", xlim = 0:1, verticals=TRUE,
         main = quote("Empirical"~ F[n](C(U)) ~
                         "and its Kendall distribution" ~ K(u)),
         do.points = FALSE, asp = 1)
    abline(0,1, lty = 2); abline(h = 0:1, v = 0:1, lty = 3, col = "gray")
    lines(u, Ku.MC, col = "red") # not quite monotone
    lines(u, Ku, col = "black")  # strictly  monotone:
    stopifnot(diff(Ku) >= 0)
    legend(.25, .75, expression(F[n], K[MC](u), K(u)),
           col=c("blue" , "red", "black"), lty = 1, lwd = 1.5, bty = "n")

    if(require("Rmpfr")) { # pK() now also works with high precision numbers:
     uM <- mpfr(0:255, 99)/256
     if(FALSE) {
       # not yet, now fails in  polyG() :
       KuM <- pK(uM, copula = cop@copula, d = d)
      ##  debug(copula:::.pK)
      debug(copula:::polyG)
     }
    }# if( Rmpfr )

    ## Testing qK
    pexpr <- quote( 0:63/63 );  p <- eval(pexpr)
    d <- 10
    cop <- onacopulaL("Gumbel", list(theta = 2, 1:d))
    system.time(qK0 <- qK(p, copula = cop@copula, d = d)) # "default" - fast

    system.time(qK1  <- qK(p, copula= cop@copula, d=d, method = "simple"))
    system.time(qK1. <- qK(p, copula= cop@copula, d=d, method = "simple", tol = 1e-12))
    system.time(qK2  <- qK(p, copula= cop@copula, d=d, method = "sort"))
    system.time(qK2. <- qK(p, copula= cop@copula, d=d, method = "sort",   tol = 1e-12))
    system.time(qK3  <- qK(p, copula= cop@copula, d=d, method = "discrete", u.grid = 0:1e4/1e4))
    system.time(qK4  <- qK(p, copula= cop@copula, d=d, method = "monoH.FC",
                           u.grid = 0:5e2/5e2))
    system.time(qK4. <- qK(p, copula= cop@copula, d=d, method = "monoH.FC",
                           u.grid = 0:5e2/5e2, tol = 1e-12))
    system.time(qK5  <- qK(p, copula= cop@copula, d=d, method = "monoH.FC",
                           u.grid = 0:5e3/5e3))
    system.time(qK5. <- qK(p, copula= cop@copula, d=d, method = "monoH.FC",
                           u.grid = 0:5e3/5e3, tol = 1e-12))
    system.time(qK6  <- qK(p, copula= cop@copula, d=d, method = "monoH.FC",
                           u.grid = (0:5e3/5e3)^2))
    system.time(qK6. <- qK(p, copula= cop@copula, d=d, method = "monoH.FC",
                           u.grid = (0:5e3/5e3)^2, tol = 1e-12))

    ## Visually they all coincide :
    cols <- adjustcolor(c("gray50", "gray80", "light blue",
                          "royal blue", "purple3", "purple4", "purple"), 0.6)
    matplot(p, cbind(qK0, qK1, qK2, qK3, qK4, qK5, qK6), type = "l", lwd = 2*7:1, lty = 1:7, col = cols,
            xlab = bquote(p == .(pexpr)), ylab = quote({K^{-1}}(u)),
            main = "qK(p, method = *)")
    legend("topleft", col = cols, lwd = 2*7:1, lty = 1:7, bty = "n", inset = .03,
           legend= paste0("method= ",
                 sQuote(c("default", "simple", "sort",
                          "discrete(1e4)", "monoH.FC(500)", "monoH.FC(5e3)", "monoH.FC(*^2)"))))

    ## See they *are* inverses  (but only approximately!):
    eqInv <- function(qK) all.equal(p, pK(qK, cop@copula, d=d), tol=0)

    eqInv(qK0 ) # "default"	       0.03  worst
    eqInv(qK1 ) # "simple"	       0.0011 - best
    eqInv(qK1.) # "simple", e-12   0.00000 (8.73 e-13) !
    eqInv(qK2 ) # "sort"	       0.0013 (close)
    eqInv(qK2.) # "sort", e-12     0.00000 (7.32 e-12)
    eqInv(qK3 ) # "discrete"       0.0026
    eqInv(qK4 ) # "monoH.FC(500)"  0.0095
    eqInv(qK4.) # "m.H.FC(5c)e-12" 0.00963
    eqInv(qK5 ) # "monoH.FC(5e3)"  0.001148
    eqInv(qK5.) # "m.H.FC(5k)e-12" 0.000989
    eqInv(qK6 ) # "monoH.FC(*^2)"  0.001111
    eqInv(qK6.) # "m.H.FC(*^2)e-12"0.00000 (1.190 e-09)

    ## and ensure the differences are not too large
    stopifnot(
     all.equal(qK0, qK1, tol = 1e-2) # !
     ,
     all.equal(qK1, qK2, tol = 1e-4)
     ,
     all.equal(qK2, qK3, tol = 1e-3)
     ,
     all.equal(qK3, qK4, tol = 1e-3)
     ,
     all.equal(qK4, qK0, tol = 1e-2) # !
    )

    stopifnot(all.equal(p, pK(qK0, cop@copula, d=d), tol = 0.04))

[Package copula version 0.999-18 Index]