mvdist-mscFit | R Documentation |
Fitting the parameters for the Multivariate Skew Cauchy Distribution.
mscFit(x, trace=FALSE, title = NULL, description = NULL)
x |
a matrix with "d" columns, giving the coordinates of the point(s) where the density must be evaluated. |
trace |
a logical value, should the estimation be traced? By default FALSE. |
title |
an optional project title. |
description |
an option project desctiption. |
This is an easy to use wrapper function using default function
settings for fitting the distributional parameters in the framework
of the contributed package "sn"
written by Adelchi Azzalini.
Starting values for the estimation have not to be provided, they are automatically created.
## Not run:
## Load Library:
require(sn)
## mscFit -
# Fit Example:
N <- 1000
xi <- c(0, 0)
Omega <- diag(2); Omega[2,1] <- Omega[1,2] <- 0.5
alpha <- c(2, -6)
set.seed(4711)
X <- rmsc(n=N, xi, Omega, alpha)
ans <- mscFit(X)
# Show fitted Parameters:
print(ans)
# 2-D Density Plot:
plot(hexBinning(X[,1], X[, 2], bins = 30), main="Skew Cauchy")
# Add Contours:
N <- 101
x <- seq(min(X[, 1]), max(X[, 1]), l=N)
y <- seq(min(X[, 2]), max(X[, 2]), l=N)
u <- grid2d(x, y)$x
v <- grid2d(x, y)$y
XY <- cbind(u, v)
param <- ans@fit$dp
Z <- matrix(dmsc(XY, param[[1]][1,], param[[2]], param[[3]]), ncol=N)
contour(x, y, Z, add=TRUE, col="green", lwd=2)
grid(col="brown", lty=3)
## Cut the Tails:
CUT <- 25
X <- X[abs(X[, 1]) <= CUT, ]
X <- X[abs(X[, 2]) <= CUT, ]
plot(hexBinning(X[,1], X[, 2], bins = 30), main="Skew Cauchy")
x <- y <- seq(-CUT, CUT, l=N)
u <- grid2d(x, y)$x
v <- grid2d(x, y)$y
XY <- cbind(u, v)
param <- ans@fit$dp
Z <- matrix(dmsc(XY, param[[1]][1,], param[[2]], param[[3]]), ncol=N)
contour(x, y, Z, add=TRUE, col="green", lwd=2)
grid(col="brown", lty=3)
# Try larger cuts ...
## End(Not run)
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