ddmvnorm {DepthProc} R Documentation

## Normal depth versus depth plot

### Description

Produces a normal DD plot of a multivariate dataset.

### Usage

```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).

### 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.

### Value

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

### Author(s)

Daniel Kosiorowski, Mateusz Bocian, Anna Wegrzynkiewicz and Zygmunt Zawadzki from Cracow University of Economics.

### 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.

`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)

# 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)

```

[Package DepthProc version 2.1.3 Index]