Dear Hector,

I would be really grateful if you could give me a hint how to calculate the
probabilities for my data. Since is 2d set it's getting quite complicated:

M$parameters

$Vinv
NULL

$pro
[1] 0.95238095 0.04761905

$mean
              [,1]      [,2]
mir.383_1 1.348992 0.7405658
mir.383_3 1.269590 0.6658261

$variance
$variance$modelName
[1] "EEE"

$variance$d
[1] 2

$variance$G
[1] 2

$variance$sigma
, , 1

            mir.383_1   mir.383_3
mir.383_1 0.004268039 0.003844819
mir.383_3 0.003844819 0.010359503

, , 2

            mir.383_1   mir.383_3
mir.383_1 0.004268039 0.003844819
mir.383_3 0.003844819 0.010359503


$variance$Sigma
            mir.383_1   mir.383_3
mir.383_1 0.004268039 0.003844819
mir.383_3 0.003844819 0.010359503

$variance$cholSigma
            mir.383_1   mir.383_3
mir.383_1 -0.06533023 -0.05885207
mir.383_3  0.00000000 -0.08304178

Dear Steve,

You're absolutely right. The data are easy to separate and there's no
problem with clustering, but I'm just curious how they behave within
particular cluster.

Thank you very much for your help. I didn't see the problem from this point
of view.

Best,
B.

2012/1/27 Hector Corrada Bravo <hcorrada@umiacs.umd.edu>

> M$parameters has means and covariance(s) (depending on the model you
> specified) for each cluster. You can compute probabilities for any
> point using these.
>
>
>
>
> On Fri, Jan 27, 2012 at 10:17 AM, Steve Lianoglou
> <mailinglist.honeypot@gmail.com> wrote:
> > Hi,
> >
> > On Fri, Jan 27, 2012 at 9:55 AM, Barbara Uszczynska
> > <uszczynska@gmail.com> wrote:
> >> Hi,
> >>
> >> Thanks for reply. The thing is that z matrix (dataset1MC$z) gives only
> 1.00,
> >>  if point is classified to particular cluster:
> >>
> >>         [,1]         [,2]
> >> NA12043 1.000000e+00 2.608455e-15
> >> NA12249 1.000000e+00 7.784309e-15
> >> NA12264 1.000000e+00 1.664289e-25
> >> NA12234 3.151495e-19 1.000000e+00
> >> NA12236 1.000000e+00 4.399892e-21
> >>
> >> It means that samples NA12043, NA12249, NA12264, NA12236 are in the same
> >> group nr 1, and NA12234 is in group nr 2, but there's no information how
> >> strong they belong to their groups.
> >
> > But isn't this, perhaps, a function of your data being easy to separate?
> >
> > For instance, if you make a synthetic (still easy to separate) 2d
> > dataset like so:
> >
> > R> set.seed(123)
> > R> x1 <- rnorm(100, -1, 1)
> > R> y1 <- rnorm(100, -1, 1)
> >
> > R> x2 <- rnorm(100, 1, 1)
> > R> y2 <- rnorm(100, 1, 1)
> >
> > You can plot it to see the "easy to split" clusters:
> >
> > R> plot(x1,y1,pch=19,cex=.7,col="blue", ylim=c(-10, 10), xlim=c(-10,10)
> > R> points(x2,y2,pch=19,cex=.7,col="red")
> >
> > Let's see what Mclust tells us:
> >
> > R> m <- rbind(cbind(x1,y1), cbind(x2,y2))
> > R> M <- Mclust(m, 2)
> >
> > Although most points have a super high probability of landing in one
> > cluster, some do not, eg:
> >
> > R> sum(apply(M$z, 1, function(row) any(row > .8)))
> > [1] 175
> >
> > So, 175 out of 200 points have a class probability assigned to them
> that's > 0.8
> >
> > -steve
> >
> > --
> > Steve Lianoglou
> > Graduate Student: Computational Systems Biology
> >  | Memorial Sloan-Kettering Cancer Center
> >  | Weill Medical College of Cornell University
> > Contact Info: http://cbio.mskcc.org/~lianos/contact
> >
> > _______________________________________________
> > Bioconductor mailing list
> > Bioconductor@r-project.org
> > https://stat.ethz.ch/mailman/listinfo/bioconductor
> > Search the archives:
> http://news.gmane.org/gmane.science.biology.informatics.conductor
>

	[[alternative HTML version deleted]]

