MissingGeoMorph: Estimate missing landmark data
In LOST: Missing Morphometric Data Simulation and Estimation

Description Usage Arguments Value Author(s) References See Also

This function provides several options for estimating landmark data (details of which can be found in the references below). The function first alignes the landmarks using Procrustes superimposition (align.missing). Both 2D and 3D coordinates can be accommodated.

1	MissingGeoMorph(x, method = "BPCA", original.scale = FALSE)

`x`	A n* l X 2 matrix (2D data only) or an l X m X n array (2D or 3D data) of coordinate data, where n is the number of specimens and l is the number of landmarks, and m is the number of dimensions. All landmarks from one specimen should be grouped together. Missing values should be given as NA
`method`	Four methods are provided for estimating missing landmark data: 1) "BPCA" - Bayesian principal component analysis, 2) "mean" - mean substitution, 3) "reg" - values are estimated based on the most strongly correlated variable available, and 4) "TPS" - thin plate spline interpolation (only available for 2D). See Arbour and Brown (2014) for a comparison of the performance of each of these methods.
`original.scale`	Rescale and translate the data back to its original size (TRUE) or leave it in the rescaled, superimposed configuration (FALSE)

Returns an n * l X 2 (or 3) matrix of coordinate data, with missing values imputed. Landmarks have been aligned and are given in the original shape space.

J. Arbour

Arbour, J. and Brown, C. 2014. Incomplete specimens in Geometric Morphometric Analyses. Methods in Ecology and Evolution 5(1):16-26.

Brown, C., Arbour, J. and Jackson, D. 2012. Testing of the Effect of Missing Data Estimation and Distribution in Morphometric Multivariate Data Analyses. Systematic Biology 61(6):941-954.

align.missing, missing.specimens

LOST documentation built on April 14, 2020, 6:18 p.m.