frechet {shapes} | R Documentation |
Mean shape estimators
Description
Calculation of different types of Frechet mean shapes, or the isotropic offset Gaussian MLE mean shape
Usage
frechet(x, mean="intrinsic")
Arguments
x |
Input k x m x n real array, where k is the number of points, m is the number of dimensions, and n is the sample size. |
mean |
Type of mean shape. The Frechet mean shape is obtained by minimizing sum d(x_i,mu)^2 with respect to mu. Different estimators are obtained with different choices of distance d. "intrinsic" intrinsic mean shape (d = rho = Riemannian distance); "partial.procrustes" partial Procrustes (d = 2*sin(rho/2)); "full.procrustes" full Procrustes (d = sin(rho)); h (positive real number) M-estimator (d^2 = (1 - cos^(2h)(rho))/h) Kent (1992); "mle" - isotropic offset Gaussian MLE of Mardia and Dryden (1989) |
Value
A list with components
mshape |
Mean shape estimate |
var |
Minimized Frechet variance (not available for MLE) |
kappa |
(if available) The estimated kappa for the MLE |
code |
Code from optimization, as given by function nlm - should be 1 or 2 |
gradient |
Gradient from the optimization, as given by function nlm - should be close to zero |
Author(s)
Ian Dryden
References
Dryden, I. L. (1991). Discussion to 'Procrustes methods in the statistical analysis of shape' by C.R. Goodall. Journal of the Royal Statistical Society, Series B, 53:327-328.
Dryden, I.L. and Mardia, K.V. (2016). Statistical Shape Analysis, with applications in R (Second Edition). Wiley, Chichester.
Kent, J. T. (1992). New directions in shape analysis. In Mardia, K. V., editor, The Art of Statistical Science, pages 115-127. Wiley, Chichester.
Mardia, K. V. and Dryden, I. L. (1989b). The statistical analysis of shape data. Biometrika, 76:271-282.
See Also
procGPA
Examples
#2D example : female and male Gorillas (cf. Dryden and Mardia, 2016)
data(gorf.dat)
frechet(gorf.dat[,,1:4],mean="intrinsic")