RSpobs {copula} | R Documentation

## Pseudo-Observations of Radial and Uniform Part of Elliptical and Archimedean Copulas

### Description

Given a matrix of iid multivariate data from a meta-elliptical or meta-Archimedean model, `RSpobs()` computes pseudo-observations of the radial part R and the vector S which follows a uniform distribution on the unit sphere (for elliptical copulas) or the unit simplex (for Archimedean copulas). These quantities can be used for (graphical) goodness-of-fit tests, for example.

### Usage

    RSpobs(x, do.pobs = TRUE, method = c("ellip", "archm"), ...)

### Arguments

`x` | an (n, d)-`matrix` of data; if `do.pobs=FALSE`, the rows of `x` are assumed to lie in the d-dimensional unit hypercube (if they do not, this leads to an error).
`do.pobs` | `logical` indicating whether `pobs()` is applied to `x` for transforming the data to the d-dimensional unit hypercube.
`method` | `character` string indicating the assumed underlying model, being meta-elliptical if `method="ellip"` (in which case `S` should be approximately uniform on the d-dimensional unit sphere) or meta-Archimedean if `method="archm"` (in which case `S` should be approximately uniform on the d-dimensional unit simplex).

`...`

additional arguments passed to the implemented methods. These can be

`method="ellip"`

  `qQg()` (the quantile `function` of the (assumed) distribution function G[g] as given in Genest, Hofert, G. Nešlehová (2014)); if provided, `qQg()` is used in the transformation for obtaining pseudo-observations of R and S (see the code for more details).

`method="archm"`

  `iPsi()` (the assumed underlying generator inverse); if provided, `iPsi()` is used in the transformation for obtaining pseudo-observations of R and S (see the code for more details).

### Details

The construction of the pseudo-obersvations of the radial part and the uniform distribution on the unit sphere/simplex is described in Genest, Hofert, G. Nešlehová (2014).

### Value

A `list` with components `R` (an n-`vector` containing the pseudo-observations of the radial part) and `S` (an (n, d)-`matrix` containing the pseudo-observations of the uniform distribution (on the unit sphere/simplex)).

### References

Genest, C., Hofert, M., G. Nešlehová, J., (2014). Is the dependence Archimedean, elliptical, or what? To be submitted.

### See Also

`pobs()` for computing the “classical” pseudo-observations.

### Examples

    set.seed(100)
    n <- 250 # sample size
    d <- 5 # dimension
    nu <- 3 # degrees of freedom

    ## Build a mean vector and a dispersion matrix,
    ## and generate multivariate t_nu data:
    mu <- rev(seq_len(d)) # d, d-1, .., 1
    L <- diag(d) # identity in dim d
    L[lower.tri(L)] <- 1:(d*(d-1)/2)/d # Cholesky factor (diagonal > 0)
    Sigma <- crossprod(L) # pos.-def. dispersion matrix (*not* covariance of X)
    X <- rep(mu, each=n) + mvtnorm::rmvt(n, sigma=Sigma, df=nu) # multiv. t_nu data
    ## note: this is *wrong*: mvtnorm::rmvt(n, mean=mu, sigma=Sigma, df=nu)

    ## compute pseudo-observations of the radial part and uniform distribution
    ## once for 1a), once for 1b) below
    RS.t    <- RSpobs(X, method="ellip", qQg=function(p) qt(p, df=nu)) # 'correct'
    RS.norm <- RSpobs(X, method="ellip", qQg=qnorm) # for testing 'wrong' distribution
    stopifnot(length(RS.norm$R) == n, length(RS.t$R) == n,
              dim(RS.norm$S) == c(n,d), dim(RS.t$S) == c(n,d))

    ## 1) Graphically testing the radial part

    ## 1a) Q-Q plot of R against the correct quantiles
    qqplot2(RS.t$R, qF=function(p) sqrt(d*qf(p, df1=d, df2=nu)),
            main.args=list(text=as.expression(substitute(bold(italic(
                           F[list(d.,nu.)](r^2/d.))~~"Q-Q Plot"),
                           list(d.=d, nu.=nu))),
                           side=3, cex=1.3, line=1.1, xpd=NA))

    ## 1b) Q-Q plot of R against the quantiles of F_R for a multivariate normal
    ##     distribution
    tit <- as.expression(substitute(bold(italic(chi[d.]) ~~ "Q-Q Plot"), list(d.=d)))
    qqplot2(RS.norm$R, qF=function(p) sqrt(qchisq(p, df=d)),
            main.args=list(text=tit, side=3, cex=1.3, line=1.1, xpd=NA))

    ## 2) Graphically testing the angular distribution

    ## auxiliary function
    qqp <- function(k, Bmat) {
        tit <- as.expression(substitute(plain("Beta")(s1,s2) ~~ bold("Q-Q Plot"),
                                        list(s1 = k/2, s2 = (ncol(Bmat)+1-k)/2)))
        qqplot2(Bmat[,k],
                qF=function(p) qbeta(p, shape1=k/2, shape2=(ncol(Bmat)+1-k)/2),
                main.args=list(text=tit, side=3, cex=1.3, line=1.1, xpd=NA))
    }

    ## 2a) Q-Q plot of the 'correct' angular distribution
    ##     (Bmat[,k] should follow a Beta(k/2, (d-k)/2) distribution)
    Bmat.t <- gofBTstat(RS.t$S)
    qqp(1, Bmat=Bmat.t) # k=1
    qqp(3, Bmat=Bmat.t) # k=3

    ## 2b) Q-Q plot of the 'wrong' angular distribution
    Bmat.norm <- gofBTstat(RS.norm$S)
    qqp(1, Bmat=Bmat.norm) # k=1
    qqp(3, Bmat=Bmat.norm) # k=3

    ## 3) Graphically check independence between radial part and B_1 and B_3

    ## 'correct' distributions (multivariate t)
    plot(pobs(cbind(RS.t$R, Bmat.t[,1])), # k = 1
              xlab=quote(italic(R)), ylab=quote(italic(B)[1]),
              main=quote(bold("Rank plot between"~~italic(R)~~"and"~~italic(B)[1])))
    plot(pobs(cbind(RS.t$R, Bmat.t[,3])), # k = 3
    	  xlab=quote(italic(R)), ylab=quote(italic(B)[3]),
              main=quote(bold("Rank plot between"~~italic(R)~~"and"~~italic(B)[3])))

    ## 'wrong' distributions (multivariate normal)
    plot(pobs(cbind(RS.norm$R, Bmat.norm[,1])), # k = 1
              xlab=quote(italic(R)), ylab=quote(italic(B)[1]),
              main=quote(bold("Rank plot between"~~italic(R)~~"and"~~italic(B)[1])))
    plot(pobs(cbind(RS.norm$R, Bmat.norm[,3])), # k = 3
    	  xlab=quote(italic(R)), ylab=quote(italic(B)[3]),
              main=quote(bold("Rank plot between"~~italic(R)~~"and"~~italic(B)[3])))

[Package copula version 0.999-15 Index]