In the area of cluster analysis I want to calculate the dissimilarity for my data (actual use case is to feed in the dissimilarities into a plotting function to calculate so called silhouette plots).
Now, one of my variables follows a Poisson distribution and I'm actually unsure what would be the best/closest variable type for this kind of data, or if necessary what steps I'd need to take to potentially convert from Poisson to sth. else?
For my purpose I'm using function
daisy from R package
cluster which requires my columns to be of the following variable types:
Columns of mode numeric (i.e. all columns when x is a matrix) will be recognized as interval scaled variables, columns of class factor will be recognized as nominal variables, and columns of class ordered will be recognized as ordinal variables. Other variable types should be specified with the type argument. Missing values (NAs) are allowed.
The list may contain the following components: "ordratio" (ratio scaled variables to be treated as ordinal variables), "logratio" (ratio scaled variables that must be logarithmically transformed), "asymm" (asymmetric binary) and "symm" (symmetric binary variables). Each component's value is a vector, containing the names or the numbers of the corresponding columns of x. Variables not mentioned in the type list are interpreted as usual (see argument x).