obliterator: Simulate missing morphometric data with anatomical bias

Description Usage Arguments Value Author(s) References See Also

View source: R/obliterator.R


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).


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



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


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


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 crocs.landmarks


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.

See Also


LOST documentation built on May 29, 2017, 6:36 p.m.