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Cluster probability and stratified random sampling of schools countrywide was adopted to evaluate how the four program components of SHAPES are being implemented in the selected schools on one hand, and measuring the impact of the four program components in the selected schools on the other.
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Principal components of shape and cortical thickness were correlated to anthropological data (age, sex, height and body mass) to produce predictive statistical models.
Uniform components of shape variation record global variations in shape (e.g., shearing) and are also generated by comparing individual configurations to the consensus.
In total, 22 components of shape variation, called relative warps, were generated from the 13 landmarks.
We use PCA to separate the various components of shape variation in our different example analyses.
Using tpsRelw software (Rohlf 2010b), we extracted relative warps (principal components of shape variation) to reduce dimensionality of the data.
Digitized landmarks were analyzed for components of shape variation in a thin-plate spline analysis using tpsRelW (Rohlf 2008).
From the 13 landmarks, 22 orthogonal components of shape variation called relative warps (RelW1-22) westimatedandd are are described below.
PCA has previously been used to analyze collections of cell shapes; we here analyze the principal components of shape change, or boundary motion.
Comparing the principal components obtained from individual and ensemble PCA allowed us to determine the cell-to-cell reproducibility of the principal components of shape and strain energy.
Similarly, we have extended the Procrustes ANOVA for quantifying the different components of shape variation [ 29, 31] for any type of symmetry.
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