# Simulate missing morphometric data with anatomical bias

### Description

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).

### Usage

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

### Arguments

`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 |

### Value

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

### Author(s)

J. Arbour and C. Brown

### References

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

`missing.data`

,`byclade`