pobs {copula}R Documentation

Pseudo-Observations

Description

Compute the pseudo-observations for the given data matrix.

Usage

pobs(x, na.last = "keep",
     ties.method = , lower.tail = TRUE)

Arguments

x

n x d-matrix (or d-vector) of random variates to be converted to pseudo-observations.

na.last, ties.method

strings, passed to rank; see there.

lower.tail

logical which, if FALSE, returns the pseudo-observations when applying the empirical marginal survival functions.

Details

Given n realizations x_i=(x_{i1},...,x_{id}), i in {1,...,n} of a random vector X, the pseudo-observations are defined via u_{ij}=r_{ij}/(n+1) for i in {1,...,n} and j in {1,...,d}, where r_{ij} denotes the rank of x_{ij} among all x_{kj}, k in {1,...,n}. The pseudo-observations can thus also be computed by component-wise applying the empirical distribution functions to the data and scaling the result by n/(n+1). This asymptotically negligible scaling factor is used to force the variates to fall inside the open unit hypercube, for example, to avoid problems with density evaluation at the boundaries. Note that pobs(, lower.tail=FALSE) simply returns 1-pobs().

Value

matrix (or vector) of the same dimensions as x containing the pseudo-observations.

Examples

## Simple definition of the function:
pobs

## Draw from a multivariate normal distribution
d <- 10
set.seed(1)
P <- Matrix::nearPD(matrix(pmin(pmax(runif(d*d), 0.3), 0.99), ncol=d))$mat
diag(P) <- rep(1, d)
n <- 500
x <- MASS::mvrnorm(n, mu = rep(0, d), Sigma = P)

## Compute pseudo-observations (should roughly follow a Gauss
## copula with correlation matrix P)
u <- pobs(x)
plot(u[,5],u[,10], xlab=expression(italic(U)[1]), ylab=expression(italic(U)[2]))

## All components: pairwise plot
pairs(u, gap=0, pch=".", labels=as.expression( sapply(1:d, function(j) bquote(italic(U[.(j)]))) ))

[Package copula version 0.999-15 Index]