ddmvnorm: Normal depth versus depth plot

Description Usage Arguments Details Value Author(s) References See Also Examples

Description

Produces a normal DD plot of a multivirate dataset.

Usage

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

Arguments

x

The data sample for DD plot.

size

size of theoretical set

robust

Logical. Dafault 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.

...

Parameters passed to depth

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.

See Also

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

Examples

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# 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)

Example output

Loading required package: ggplot2
Loading required package: Rcpp
Loading required package: rrcov
Loading required package: robustbase
Scalable Robust Estimators with High Breakdown Point (version 1.4-3)

Loading required package: MASS
Loading required package: np
Nonparametric Kernel Methods for Mixed Datatypes (version 0.60-3)
[vignette("np_faq",package="np") provides answers to frequently asked questions]
[vignette("np",package="np") an overview]
[vignette("entropy_np",package="np") an overview of entropy-based methods]

Attaching package: 'DepthProc'

The following object is masked from 'package:base':

    as.matrix

DDPlot

Depth Metohod:
	 ProjectionDDPlot

Depth Metohod:
	 Projection

DepthProc documentation built on May 31, 2017, 4:49 a.m.