adtestWrapper {robCompositions}R Documentation

Wrapper for Anderson-Darling tests

Description

A set of Anderson-Darling tests (Anderson and Darling, 1952) are applied as proposed by Aitchison (Aichison, 1986).

Usage

adtestWrapper(x, alpha = 0.05, R = 1000, robustEst = FALSE)

Arguments

x compositional data of class data.frame or matrix
alpha significance level
R Number of Monte Carlo simulations in order to provide p-values.
robustEst logical

Details

First, the data is tranformed using the ‘ilr’-transformation. After applying this tranformation

- all (D-1)-dimensional marginal, univarite distributions are tested using the univariate Anderson-Darling Test for normality.

- all 0.5 (D-1)(D-2)-dimensional bivariate angle distributions are tested using the Anderson-Darling angle Test for normality.

- the (D-1)-dimensional radius distribution is tested using the Anderson-Darling radius Test for normality.

Value

res a list including each test result
check information about the rejection of the Null hypothesis
alpha the underlying significance level
info further information which is used by the print and summary method.
est standard for standard estimations and robust for robust estimation

Author(s)

Matthias Templ and Karel Hron

References

Anderson, T.W. and Darling, D.A. (1952) Asymptotic theory of certain goodness-of-fit criteria based on stochastic processes Annals of Mathematical Statistics, 23 193-212.

Aitchison, J. (1986) The Statistical Analysis of Compositional Data Monographs on Statistics and Applied Probability. Chapman & Hall Ltd., London (UK). 416p.

See Also

adtest, ilr

Examples

data(machineOperators)
a <- adtestWrapper(machineOperators, R=50) # choose higher value of R
a
summary(a)

[Package robCompositions version 1.2.2 Index]