This function simulates the effect of proximity between measurements in morphometric data on the distribution of missing values. This attempts to replicate specimens showing regional deformation or incompleteness. From a morphometric dataset, this function selects a number of specimens to have data points removed from and a number of measurements to remove from each of these specimens based on the distribution of missing data produced by `missing.data`

. For each specimen, this function randomly selects one starting data point for removal. All subsequent data points have a probability of removal that is proportional to the inverse of the distance to all previously removed data points, based on a reference set of landmarks (matrix 'distances'). For a complete mathematical description see Brown et al. (In Press).

1 | ```
obliterator(x, remperc, landmarks, expo=1)
``` |

`x` |
A n X m matrix of morphometric data with n specimens and m variables |

`remperc` |
The percentage of data to be removed from the matrix, expressed as a decimal (ex: 30 percent would be entered as 0.3) |

`landmarks` |
A 6 X m matrix that includes the start and end points (landmarks) for each morphometric measurement from a reference specimen (3D). The data in each column is ordered as x1,x2,y1,y2,z1,z2. See example |

`expo` |
An optional term for raising the denominator to an exponent, to increase or decrease the severity of the anatomical bias |

Returns a n X m matrix of morphometric data with missing variables input as NA

J. Arbour and C. Brown

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.

`missing.data`

,`byclade`

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

All documentation is copyright its authors; we didn't write any of that.