pnss3d: Principal Nested Shape Space Analysis

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

Description

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

Usage

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pnss3d(x, sphere.type = "seq.test", alpha = 0.1, R = 100, nlast.small.sphere = 0,n.pc=3)

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 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)

Value

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.

Author(s)

Kwang-Rae Kim, Ian Dryden

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.

See Also

pns, pns4pc, plot3darcs

Examples

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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")

shapes documentation built on March 31, 2021, 5:09 p.m.

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