pnss3d function

Principal Nested Shape Space Analysis

Approximation of Principal Nested Shapes Spaces using PCA: 2D or 3D data, small or large samples

pnss3d(x,sphere.type="seq.test",alpha = 0.1,R = 100,
                        nlast.small.sphere = 1,n.pc="Full",output=TRUE)

Arguments

• 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 last part. 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)
• output: Logical. If TRUE then plots and some brief printed summaries are given. If FALSE then no plots or output is given.

Returns

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.

References

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.

Author(s)

Kwang-Rae Kim, Ian Dryden

See Also

pns, pns4pc, plot3darcs

Examples

ans <- pnss3d(digit3.dat, sphere.type="BIC", n.pc=5)
#aa <- plot3darcs(ans,c=2,pcno=1)
#bb <- plot3darcs(ans,c=2,pcno=1,type="pca")