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robCompositions (version 1.9.1)

adtestWrapper: 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

Value

  • resa list including each test result
  • checkinformation about the rejection of the null hypothesis
  • alphathe underlying significance level
  • infofurther information which is used by the print and summary method.
  • eststandard for standard estimation and robust for robust estimation

Details

First, the data is transformed using the ilr-transformation. After applying this transformation - all (D-1)-dimensional marginal, univariate 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.

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, isomLR

Examples

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

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