Principal Nested Shape Spaces from PCA
Approximation of Principal Nested Shapes Spaces using PCA
pns4pc(x, sphere.type = "seq.test", alpha = 0.1, R = 100, nlast.small.sphere = 1,n.pc=2)
x: k x m x n array of landmark data.sphere.type: a character string specifying the type of sphere fitting method. "seq.test" specifies sequential tests to decide either "small" or "great"; "small" specifies Principal Nested SMALL Sphere; "great" specifies Principal Nested GREAT Sphere (radius pi/2); "BIC" specifies BIC statistic to decide either "small" or "great"; and "bi.sphere" specifies Principal Nested GREAT Sphere for the first part and Principal Nested SMALL Sphere for The default is "seq.test".alpha: significance level (0 < alpha < 1) used when sphere.type = "seq.test". The default is 0.1.R: the number of bootstrap samples to be evaluated for the sequential test. The default is 100.nlast.small.sphere: the number of small spheres in the finishing part used when sphere.type = "bi.sphere".n.pc: the number of PC scores to be used (n.pc >= 2)A list with components - PNS: the output of the function pns
GPAout: the result of GPA
spheredata: transformed spherical data from the PC scores
percent: proportion of variances explained.
Dryden, I.L., Kim, K., Laughton, C.A. and Le, H. (2019). Principal nested shape space analysis of molecular dynamics data. Annals of Applied Statistics, 13, 2213-2234.
Jung, S., Dryden, I.L. and Marron, J.S. (2012). Analysis of principal nested spheres. Biometrika, 99, 551-568.
Kwang-Rae Kim
pns, pns4pc, pnss3d, plot3darcs
pns4pc(digit3.dat,n.pc=2)