rp.sample: Generates a test statistics sample of random projections

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

View source: R/rp_test.R


Generates a sample of test statistics using k independent random projections of a stationary process. The first half values of the sample, are estimated using a Lobato and Velasco's statistic test. The last half values with an Epps statistic test.


rp.sample(y,k = 16,pars1 = c(100,1),pars2 = c(2,7),seed = NULL)



a numeric vector or an object of the ts class containing a stationary time series.


an integer with the number of random projections to be used, by default k = 16.


an optional real vector with the shape parameters of the beta distribution used for the odd number random projection. By default, pars1 = c(100,1) where, shape1 = 100 and shape2 = 1.


an optional real vector with the shape parameters of the beta distribution used for the even number random projection. By default, pars2 = c(2,7) where, shape1 = 2 and shape2 = 7.


An optional seed to use.


The rp.sample function generates k independent random projections of the process. A Lobatos and Velasco's test is applied to the first half of the projections. And an Epps test for the other half.

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 values.

The test was proposed by Nieto-Reyes, A.,Cuesta-Albertos, J. & Gamboa, F. (2014).


A list with 2 real value vectors:


Alicia Nieto-Reyes and Asael Alonzo Matamoros


Nieto-Reyes, A., Cuesta-Albertos, J. & Gamboa, F. (2014). A random-projection based test of Gaussianity for stationary processes. Computational Statistics & Data Analysis, Elsevier, vol. 75(C), pages 124-141.

Epps, T.W. (1987). Testing that a stationary time series is Gaussian. The Annals of Statistic. 15(4), 1683-1698.

Lobato, I., & Velasco, C. (2004). A simple test of normality in time series. Journal of econometric theory. 20(4), 671-689.

See Also

lobato.test epps.test


# Generating an stationary arma process
y = arima.sim(100,model = list(ar = 0.3))
rp.test(y,k = 4)

nortsTest documentation built on Aug. 16, 2021, 5:06 p.m.