[BioC] probability of a point membership to a certain cluster

[Hector Corrada Bravo]

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 at gmail.com> wrote:
> Hi,
>
> On Fri, Jan 27, 2012 at 9:55 AM, Barbara Uszczynska
> <uszczynska at 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
>
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