ProcGPA: Workhorse function for procSym, responsible for Procrustes registration

Description

Workhorse function for procSym, responsible for Procrustes registration

Usage

ProcGPA(dat.array, tol = 1e-05, scale = TRUE, CSinit = FALSE,
  silent = FALSE, weights = NULL, centerweight = FALSE,
  reflection = TRUE)

Arguments

dat.array

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.

tol

numeric: Threshold for convergence during iterative superimpositioning.

scale

logical: indicating if scaling is requested

CSinit

logical: if TRUE, all configurations are initially scaled to Unit Centroid Size.

silent

logical: suppress output of elapsed time.

weights

numeric vector: assign per landmark weights.

centerweight

logical: if TRUE, the landmark configuration is scaled according to weights during the rotation process, instead of being scaled to the Centroid size.

reflection

logical: allow reflections.

Value

returns a list with
rotatedk x m x n array of the rotated configurations
mshapesample meanshape

References

Goodall C. 1991. Procrustes methods in the statistical analysis of shape. Journal of the Royal Statistical Society. Series B. Statistical Methodology 53:285-239.

Dryden IL, Mardia KV. 1998. Statistical shape analysis. John Wiley and sons, Chichester.

Examples

Run this code

data(boneData)
proc <- ProcGPA(boneLM, CSinit=TRUE, silent=TRUE)
#now we landmarks 5 - 9 double the weight as  the others
weights <- c(rep(1,4),rep(2,5),1)
proc.wt <- ProcGPA(boneLM, CSinit=TRUE, weights=weights, silent=TRUE)