# Estimate missing landmark data

### Description

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.

### Usage

1 | ```
MissingGeoMorph(x, nlandmarks, method = "BPCA")
``` |

### Arguments

`x` |
A n* l X 2 matrix of coordinate data, where n is the number of specimens and l is the number of landmarks. All landmarks from one specimen should be grouped together. Missing values should be given as NA |

`nlandmarks` |
The number of landmarks per specimen. |

`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. |

### Value

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.

### Author(s)

J. Arbour

### References

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.

### See Also

`align.missing`

, `missing.specimens`