Package: Gendis2unmix 0.1.1

Gendis2unmix: Calculates a generalized discriminant function to unmix two classes, typically sexes of birds

The goal of Gendis2unmix is to sex birds from a population on the basis of several measurements. The key feature is that the birds from different populations may differ in size but that within populations females are smaller than males (or reversely). The predict function for a set of unsexed birds from a new population therefore estimates a new cutoff value which thus depends on the sizes of the birds in the new population. In the training phase, a generalized discriminant function (GDF) is calculated from a birds of known sex of different populations, in which the algorithm uses a common within-covariance matrix across populations and sexes. In the prediction phase Gendis2unmix then applies the GDF to measurements of individuals of unknow sex or class. The cutoff value is determined by unmixing the distribution in terms of two normal distributions with unequal means and variances using an EM algorithm. The parametric approach taken in Gendis2unmix make it suitable for small number of samples in both the training and prediction phase (say 20-100 per sex/population).

Authors:Cajo J.F. ter Braak