View source: R/special_grassmann_utest.R
grassmann.utest | R Documentation |
Given the data on Grassmann manifold Gr(k,p), it tests whether the data is distributed uniformly.
grassmann.utest(grobj, method = c("Bing", "BingM"))
grobj |
a S3 |
method |
(case-insensitive) name of the test method containing
|
a (list) object of S3
class htest
containing:
a test statistic.
p-value under H_0.
alternative hypothesis.
name of the test.
name(s) of provided sample data.
chikuse_statistics_2003Riemann
\insertRefmardia_directional_1999Riemann
wrap.grassmann
#------------------------------------------------------------------- # Compare Bingham's original and modified versions of the test # # Test 1. sample uniformly from Gr(2,4) # Test 2. use perturbed principal components from 'iris' data in R^4 # which is concentrated around a point to reject H0. #------------------------------------------------------------------- ## Data Generation # 1. uniform data myobj1 = grassmann.runif(n=100, k=2, p=4) # 2. perturbed principal components data(iris) irdat = list() for (n in 1:100){ tmpdata = iris[1:50,1:4] + matrix(rnorm(50*4,sd=0.5),ncol=4) irdat[[n]] = eigen(cov(tmpdata))$vectors[,1:2] } myobj2 = wrap.grassmann(irdat) ## Test 1 : uniform data grassmann.utest(myobj1, method="Bing") grassmann.utest(myobj1, method="BingM") ## Tests : iris data grassmann.utest(myobj2, method="bINg") # method names are grassmann.utest(myobj2, method="BiNgM") # CASE - INSENSITIVE !
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