projShapes | R Documentation |
Helper function for plotting the projections of the shapes. It displays the projection on the xy plane of the recorded points and mean shape for a given cluster. To that end, first it is needed to carry out a generalized Procrustes analysis in the cluster to obtain the full Procrustes rotated data.
projShapes(clust,array3D,asig,prototypes)
clust |
Cluster for which represent its mean shape together with the recorded points. |
array3D |
Array with the 3D landmarks of the sample points. Each row corresponds to an observation, and each column corresponds to a dimension (x,y,z). |
asig |
Clustering optimal results. |
prototypes |
Vector of optimal prototypes. |
Numerical vector with the percentile values of an archetypoid.
Guillermo Vinue
Vinue, G., Simo, A., and Alemany, S., (2016). The k-means algorithm for 3D shapes with an application to apparel design, Advances in Data Analysis and Classification 10(1), 103–132.
LloydShapes
, HartiganShapes
, trimmedLloydShapes
landmarksNoNa <- na.exclude(landmarksSampleSpaSurv) dim(landmarksNoNa) #[1] 574 198 numLandmarks <- (dim(landmarksNoNa)[2]) / 3 #[1] 66 #As a toy example, only the first 15 individuals are used. landmarksNoNa_First10 <- landmarksNoNa[1:10, ] (numIndiv <- dim(landmarksNoNa_First10)[1]) #[1] 10 array3D <- array3Dlandm(numLandmarks, numIndiv, landmarksNoNa_First10) #shapes::plotshapes(array3D[,,1]) #calibrate::textxy(array3D[,1,1], array3D[,2,1], labs = 1:numLandmarks, cex = 0.7) numClust <- 2 ; algSteps <- 1 ; niter <- 1 ; stopCr <- 0.0001 resLL <- LloydShapes(array3D, numClust, algSteps, niter, stopCr, FALSE, FALSE) clust_kmeansProc <- resLL$asig prototypes <- anthrCases(resLL) projShapes(1, array3D, clust_kmeansProc, prototypes) #legend("topleft", c("Registrated data", "Mean shape"), pch = 1, col = 1:2, text.col = 1:2) #title("Procrustes registrated data for cluster 1 \n with its mean shape superimposed", # sub = "Plane xy")
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