What i want to do is to use an hierarchical cluster analysis on q data.frame, but using data$c as a weighting variable, could it be done? or is there a package that would let me use my weights in the clustering process, but an hierarchical process?
say i wanted to t.test data$d, data$e but having again data$c as weights, how could it be done?
and the last 2 questions:
1. how can i weight a whole dataframe in order for me to keep my weights for a specific analysis, like cluster or t.test or any other analysis that does not let me incorporate a "weight" option? I am looking for something like in spss where i can weight a whole data frame and use it for a subsequent analysis, or something like the survey package from R but one that offers flexibility to use any analysis that i want (i saw that survey package offers limited connectivity to such analyses )
2. why does a kmeans cluster analysis offer a multitude of different results?
I tried both several times
>cclust(scale(q), 3, verbose=T)
and they both seem vary unstable even with this small data.frame with respect to the cluster sizing, and i don't know why? Does it always behave like this ?