pnss3d | R Documentation |
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)
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. |
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. |
Kwang-Rae Kim, Ian Dryden
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.
pns, pns4pc, plot3darcs
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")
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