Hi R users!
I have a bit of a problem with using an hierarchical clustering algorithm:
a<-c(1:15)
b<-rep(seq(1:3), 5)
c<-rnorm(15, 0,1)
d<-c(sample(1:100, 15, replace=T))
e<-c(sample(1:100, 15, replace=T))
f<-c(sample(1:100, 15, replace=T))
data<-data.frame(a,b,c,d,e,f)
q<-data.frame(data$d, data$e, data$f)
q<-scale(q)
What i want to do is to use an hierarchical cluster analysis on q data.frame,
but using data$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?
Another question:
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)
>kmeans(scale(q), 3)
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
?
Thank you and have a great day!!
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