Description Usage Arguments Details Value Author(s) References See Also Examples
Performs the random projection test for normality. The null hypothesis (H0) is that the given data follows a stationary Gaussian process, and k is the number of used random projections.
1 
y 
a numeric vector or an object of the 
k 
an integer with the number of random projections to be used, by default

FDR 
a logical value for mixing the pvalues using a dependent False discovery
rate method. By default 
pars1 
an optional real vector with the shape parameters of the beta distribution
used for the odd number random projection. By default, 
pars2 
an optional real vector with the shape parameters of the beta distribution
used for the even number random projection. By default, 
seed 
An optional 
The random projection test generates k independent random projections of the process.
A Lobato and Velasco's test are applied to the first half of the projections, and an
Epps test for the other half. By default, a Monte Carlo pvalue estimate is used for
mixing the tests. A False discovery rate can be used for mixing by setting FDR = TRUE
.
For generating the k random projections a beta distribution is used. By default a
beta(shape1 = 100,shape = 1)
and a beta(shape1 = 2,shape = 7)
are used
to generate the odd and even projections respectively. For using a different parameter
set, change pars1
or pars2
.
The test was proposed by NietoReyes, A.,CuestaAlbertos, J. & Gamboa, F. (2014).
a h.test class with the main results of the Epps hypothesis test. The h.test class have the following values:
"k"The number of used projections
"lobato"The average Lobato and Velasco's test statistics of the k projected samples
"epps"The average Epps test statistics of the k projected samples
"p.value"The mixed p value
"alternative"The alternative hypothesis
"method"The used method: rp.test
"data.name"The data name.
Asael Alonzo Matamoros and Alicia NietoReyes.
NietoReyes, A., CuestaAlbertos, J. & Gamboa, F. (2014). A randomprojection based test of Gaussianity for stationary processes. Computational Statistics & Data Analysis, Elsevier, vol. 75(C), pages 124141.
Epps, T.W. (1987). Testing that a stationary time series is Gaussian. The Annals of Statistic. 15(4), 16831698.
Lobato, I., & Velasco, C. (2004). A simple test of normality in time series. Journal of econometric theory. 20(4), 671689.
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