Produces a normal DD plot of a multivariate dataset.

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

## Arguments

x |
The data sample for DD plot. |

size |
size of theoretical set |

robust |
Logical. Default `FALSE` . If `TRUE` , robust measures are used to specify the parameters of theoretical distribution. |

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). |

## Value

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

.

## Details

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.

## References

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.

## See also

`ddPlot`

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

## Examples

# 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