View source: R/inference_fanova.R
riem.fanova | R Documentation |
Given sets of manifold-valued data X^{(1)}_{1:{n_1}}, X^{(2)}_{1:{n_2}}, …, X^{(m)}_{1:{n_m}}, performs analysis of variance to test equality of distributions. This means, small p-value implies that at least one of the equalities does not hold.
riem.fanova(..., maxiter = 50, eps = 1e-05) riem.fanovaP(..., maxiter = 50, eps = 1e-05, nperm = 99)
... |
S3 objects of |
maxiter |
maximum number of iterations to be run. |
eps |
tolerance level for stopping criterion. |
nperm |
the number of permutations for resampling-based test. |
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.
dubey_frechet_2019Riemann
#------------------------------------------------------------------- # Example on Sphere : Uniform Samples # # Each of 4 classes consists of 20 uniform samples from uniform # density on 2-dimensional sphere S^2 in R^3. #------------------------------------------------------------------- ## PREPARE DATA OF 4 CLASSES ndata = 200 class1 = list() class2 = list() class3 = list() class4 = list() for (i in 1:ndata){ tmpxy = matrix(rnorm(4*2, sd=0.1), ncol=2) tmpz = rep(1,4) tmp3d = cbind(tmpxy, tmpz) tmp = tmp3d/sqrt(rowSums(tmp3d^2)) class1[[i]] = tmp[1,] class2[[i]] = tmp[2,] class3[[i]] = tmp[3,] class4[[i]] = tmp[4,] } obj1 = wrap.sphere(class1) obj2 = wrap.sphere(class2) obj3 = wrap.sphere(class3) obj4 = wrap.sphere(class4) ## RUN THE ASYMPTOTIC TEST riem.fanova(obj1, obj2, obj3, obj4) ## RUN THE PERMUTATION TEST WITH MANY PERMUTATIONS riem.fanovaP(obj1, obj2, obj3, obj4, nperm=999)
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