xxyy.to.array

Convert a matrix of landmark coordinates into a three-dimensional array

Compute bending energies, principal warps, partial warp scores, and the non-affine component of shape variation for 2D landmark configurations, as well as Mardia-Dryden distributions and self-similar distributions of landmarks, as described in Mitteroecker et al. (2020) <doi:10.1093/sysbio/syaa007>. Working examples to decompose shape variation into small-scale and large-scale components, and to decompose the total shape variation into outline and residual shape components are provided. Two landmark datasets are provided, that quantify skull morphology in humans and papionin primates, respectively from Mitteroecker et al. (2020) <doi:10.5061/dryad.j6q573n8s> and Grunstra et al. (2020) <doi:10.5061/dryad.zkh189373>.

Anne Le Maitre

prWarp

Warping Landmark Configurations

Silvester Bartsch

Nicole Grunstra

Philipp Mitteroecker

xxyy.to.array function

<dl><dt>M</dt>
<dd>A matrix of dimension n x [p x k] containing landmark coordinates for a set of specimens.
Each row contains all landmark coordinates for a single specimen.
The first columns correspond to the X coordinates for all landmarks, etc.</dd>
<dt>p</dt>
<dd>Number of landmarks</dd>
<dt>k</dt>
<dd>Number of dimensions (2 or 3)</dd></dl>

Arguments

Convert landmark data matrix into array (p x k x n) — xxyy.to.array

<dl>

<dt>M</dt>
<dd>A matrix of dimension n x [p x k] containing landmark coordinates for a set of specimens.
Each row contains all landmark coordinates for a single specimen.
The first columns correspond to the X coordinates for all landmarks, etc.</dd>


<dt>p</dt>
<dd>Number of landmarks</dd>


<dt>k</dt>
<dd>Number of dimensions (2 or 3)</dd>

</dl>

xxyy.to.array: Convert landmark data matrix into array (p x k x n)

Description

Usage

Value

Arguments

See Also

Examples