I would like to find a hierarchical-clustering method useful to assign a group membership into k groups for all individuals in my dataset. I have considered several classic ordination methods, PCA, NMDS, "mclust", etc., but three of my variables are categorical (see data description below). Further, I was wondering if it is preferable to a method that reports a posterior probability of group membership for each individual? I am using R.
Data description: I have sampled almost 2000 individual birds (single species representing two subspecies or phenotypes) across Sweden. All individuals are adult males. Although this is one species, in middle of Sweden there is a (migratory) divide where the southern individuals presumably migrate to West Africa and north of the divide they presumably migrate to East Africa. There is a zone of overlap approximately 300 km wide at the migratory divide.
- Wing (mm) - continuous
- Tail (mm) - continuous
- Bill-head (mm) - continuous
- Tarsus (mm) - continuous
- Mass (g) - continuous
- Colour (9 levels) - categorical
- Stable carbon-isotopes (parts per mil) - continuous
- Stable nitrogen-istopes (parts per mil) - continuous
- SNP WW1 (0, 1, 2) - molecular marker, 0 and 2 are fixed and 1 is heterozygote
- SNP WW2 (0, 1, 2) - molecular marker, 0 and 2 are fixed and 1 is heterozygote
Description of the colour variable: (brightest yellow) S+, S, S-, M+, M (medium), M-, N+, N, N- (dullest yellow-grey)