Sreedevi Gopalan wrote:> we have implemented teh following code for determinging the clustering
> model of a dataset.
>
> bicvals <- EMclust( hdata, 7)
> sumry1 <- summary(bicvals, hdata,7) # summary object for emclust()
> print(sumry1)
>
> This set of code gives the following output
>
> classification table:
>
> 1 2 3 4 5 6 7
> 1 1 1 4 1 1 1
>
> which I think means there is 1 gene in the 1st cluster...1 gene in the
> 2nd cluster , 4 genes in the 4th cluster and so on....But I need to know
> which gene is in which cluster.
>
> Is there any way to find that out....
>
Here's an example:
R> data(iris)
R> irisMatrix <- as.matrix(iris[,1:4])
R> irisBic <- EMclust(irisMatrix)
R> irisBest <- summary(irisBic, irisMatrix)
R> names(irisBest)
[1] "bic" "options"
[3] "classification" "uncertainty"
[5] "n" "d"
[7] "G" "z"
[9] "mu" "sigma"
[11] "decomp" "pro"
[13] "loglik" "Vinv"
[15] "modelName"
R> irisBest$classification
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[23] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[45] 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[67] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[89] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[111] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[133] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
R>
Hope this helps,
Sundar