Produces a normal DD plot of a multivariate dataset.

ddMvnorm( x, size = nrow(x), robust = FALSE, alpha = 0.05, title = "ddMvnorm", depth_params = list() )

x | The data sample for DD plot. |
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size | size of theoretical set |

robust | Logical. Default |

alpha | cutoff point for robust measure of covariance. |

title | title of a plot. |

depth_params | list of parameters for function depth (method, threads, ndir, la, lb, pdim, mean, cov, exact). |

Returns the normal depth versus depth plot of multivariate dataset `x`

.

In the first step the location and scale of x are estimated and theoretical sample from normal distribution with those parameters is generated. The plot presents the depth of empirical points with respect to dataset x and with respect to the theoretical sample.

Liu, R.Y., Parelius, J.M. and Singh, K. (1999), Multivariate analysis by data depth: Descriptive statistics, graphics and inference (with discussion), Ann. Statist., 27, 783--858.

Liu, R.Y., Singh K. (1993), A Quality Index Based on Data Depth and Multivariate Rank Test, *Journal of the American Statistical Association* vol. 88.

`ddPlot`

to generate ddPlot to compare to datasets or to compare a dataset with other distributions.

# EXAMPLE 1 norm <- mvrnorm(1000, c(0, 0, 0), diag(3)) con <- mvrnorm(100, c(1, 2, 5), 3 * diag(3)) sample <- rbind(norm, con) ddMvnorm(sample, robust = TRUE)#> DDPlot#> #> Depth Metohod: #> Projection# EXAMPLE 2 data(under5.mort, inf.mort, maesles.imm) data1990 <- na.omit(cbind(under5.mort[, 1], inf.mort[, 1], maesles.imm[, 1])) ddMvnorm(data1990, robust = FALSE)#> DDPlot#> #> Depth Metohod: #> Projection