search for: irisb

...ter accordind to the results of the previous cited function. 1) When trying the following example (see ?mclustModel), I get negative BIC computed by 'mclustBIC', and the best model according to the results of 'mclustModel' is the one with the highest BIC (i.e. the closer to zero). irisBIC <- mclustBIC(iris[,-5]) plot(irisBIC) mclustModel(iris[,-5], irisBIC) Because I don't find anything about this point, could someone confirm that when the BIC are positive, we try to the minimize the criterion (the model with the smallest BIC is the best one) but when the BIC are negative...

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 ,