1d:T7ff,# Multivariate Skew Cauchy Parameter Estimation Fitting the parameters for the Multivariate Skew Cauchy Distribution. ```r mscFit(x, trace=FALSE, title = NULL, description = NULL) ``` ## Arguments - `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. ## Details 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. ## Examples ```r ## 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 r

mvdist-mscFit function