RPS provides a set of tools to perform a rather complete descriptive landmark-based resistant shape analysis 3D and 2D, following Torcida et al. 2014 ("An integrated approach for landmark-based resistant shape analysis in 3D", Evol. Biol. 41(2):351_366). More specifically, these tools enable to obtain: i) a generalized resistant Procrustes superposition (robgit_RPS.R) for a set of configurations of landmarks either in 3D and 2D; ii) a resistant distance (resdistance_RPS.R) to quantify shape differences obtained following the resistant Procrustes superimposition, and iii) a resistant ordination (resunivMDS_RPS.R) of the superimposed configurations based on the universal Multidimensional Scaling from (Agarwal et al. 2010). Corresponding least squares (LS) counterparts of all these tools (procrustesCM_RPS.R, cmdistance_RPS.R and eucunivMDS_RPS.R, respectively) have also been implemented in RPS_R to offer a more complete and self-contained set of shape analysis descriptive tools. This enables the comparison of the LS and resistant superimposition results when applied to the same dataset. Also included is a rather new method for a resistant analysis of individual shape asymmetry for configurations of landmarks in 2D with bilateral symmetry (matching or object symmetry), following Torcida et al. 2016 ("A resistant method for landmark-based analysis of individual asymmetry in two dimensions",Quant. Biol. 4(4):270_282). The main tools enable to estimate the resistant symmetric shape under matching symmetry (matchingsymm_RPS.R) adn the resistant symmetric shape estimation under object symmetry (objectsymm_RPS.R). In both cases, a plot of the results and the table sof landmarks contributions to asymmetry are also offered.
A s-dimensional array of n x k matrices (k configurations of n landmarks), each representing the shape of an object
A logical value that determines if the consensus configuration is returned.
s-dimensional array of n x k matrices, representing the (resistant) superimposed objects
eucunivMDS_RPS, resunivMDS_RPS, cmdistance_RPS, resdistance_RPS, readlandtxtMorphJ_RPS, robgit_RPS,matchingsymm_RPS,objectsymm_RPS, procrustesCM_RPS
Guillermo Pacheco, Viviana Ferraggine, Sebastian Torcida
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source = array(matrix(nrow = 8,ncol = 3),c(8,3,3),dimnames = NULL) source[,,1] <- matrix(c(3,0,0,3,0,1,3,1,1,3,1,0,0,0,0,0,0,1,0,1,1,0,1,0) ,nrow = 8,ncol = 3,byrow = TRUE) source[,,2] <- matrix(c(3, 0 ,0,3, 0, 0.5,3, 1 ,0.75,3 ,1 ,0,0 ,0 ,0,0, 0 ,1,0, 1, 1,0, 1, 0.25) ,nrow = 8,ncol = 3,byrow = TRUE) source[,,3] <- matrix(c(5, 2 ,1,3, 0, 1.5,3.4, 1 ,1.75,3 ,1 ,0,0 ,0 ,0,0, 2 ,1,0, 3, 1,0, 1, 0.75) ,nrow = 8,ncol = 3,byrow = TRUE) result <- RPS::robgit_RPS(source, consenso = FALSE) result
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