geomqu2d_norm2: On Affine Equivariant Multivariate Quantiles

In bivquant: Estimation of Bivariate Quantiles

Description

This function fits empirical bivariate quantiles as proposed by Chakraborty, B. (2001). 2 dimensional data and with p=2

Usage

geomqu2d_norm2(data, probs, alpha, k = 8)

Arguments

data	must be a (n,2) matrix of observations.
probs	vector of probs which is used to calculate the us
alpha	missing or a vector of length 3 with distict values form 1 to n
k	number of us per prob

Author(s)

Nadja Klein.

References

Chakraborty, B. (2001). On affine equivariant multivariate quantiles. Annals of the Institute of Statistical Mathematics, 53, 380–403.

See Also

geomqu for details.

Examples

require("MASS")
require("mvtnorm")
set.seed(42)
n <- 50
mu <- c(6, 10)
#correlated responses
rho <- 0.5
Sigma <- matrix(c(
    1.0, rho,
    rho, 1.0
  ),
  ncol=2, byrow=TRUE)

X <- rmvnorm(n, mu, Sigma)
result <- geomqu2d_norm2(X, probs=c(0.8,0.9), k=8)
plot(result)

#now independent responses
rho <- 0.0
Sigma <- matrix(c(
    1.0, rho,
    rho, 1.0
  ),
  ncol=2, byrow=TRUE)

X <- rmvnorm(n, mu, Sigma)
result <- geomqu2d_norm2(X, probs=c(0.8,0.9), k=8)
plot(result)

#now some non-normal data
X <- dgp_cop(n, family="clayton", margins=c("norm", "norm"),
		 paramMargins=list(list(mean = 4, sd = 1), list(mean = 4, sd = 5)),
		 rho=1.75)
result <- geomqu2d_norm2(X, probs=c(0.8,0.9), k=8)
plot(result)

bivquant documentation built on Aug. 28, 2019, 5:05 p.m.