pns4pc: Principal Nested Shape Spaces from PCA

Description

Approximation of Principal Nested Shapes Spaces using PCA

Usage

pns4pc(x, sphere.type = "seq.test", alpha = 0.1, R = 100, nlast.small.sphere = 0,n.pc=2)

Arguments

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.

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.

References

Dryden, I.L., Kim, K. and Le, H. (2018). Principal nested shape spaces, with applications to molecular dynamics data. Technical report.

Jung, S., Dryden, I.L. and Marron, J.S. (2012). Analysis of principal nested spheres. Biometrika, 99, 551-568.

Examples

Run this code

# NOT RUN {
pns4pc(digit3.dat,n.pc=2)

# }