[R] simulating Gaussian Mixture Method
m_olshansky at yahoo.com
Mon Jun 16 07:59:56 CEST 2008
You should not add the 3 six dimensional variables!!!
By adding them you are getting a multivariate normal variable and not a mixture!
To get a mixture with probabilities p1 for the first, p2 for the second and p3 for the third one (p1+p2+p3=1), simulate a [0,1] uniform variable X and return the first one if X < p1, the second one if p1 <= X < p1+p2 and the third one if X >= p1+p2.
--- On Mon, 16/6/08, Peng Jiang <jp021 at sjtu.edu.cn> wrote:
> From: Peng Jiang <jp021 at sjtu.edu.cn>
> Subject: [R] simulating Gaussian Mixture Method
> To: R-help at r-project.org
> Received: Monday, 16 June, 2008, 3:48 PM
> I have a mixture pdf which has three components, each
> satisfies the
> 6 dimension normal distribution.
> I use mvrnorm() from the MASS library to generate 1000
> samples for
> each component and I add them
> to get the random samples which satisfies with the
> I use Mclust() from the mclust library to get the model
> of the
> samples and strange things happened.
> First it gave a warning
> > samplesMclust <- Mclust( samples )
> Warning messages:
> 1: In summary.mclustBIC(Bic, data, G = G, modelNames =
> modelNames) :
> best model occurs at the min or max # of components
> 2: In Mclust(samples) : optimal number of clusters occurs
> at min choice
> Then I input
> > samplesMclust
> best model: XXI with 1 components
> it says the best model is with 1 component !
> I am confused ... Is it because the way that I generate
> samples is
> thanks so much !
> Peng Jiang
> Ph.D. Candidate
> Antai College of Economics & Management
> Department of Mathematics
> Shanghai Jiaotong University (Minhang Campus)
> 800 Dongchuan Road
> 200240 Shanghai
> P. R. China
> R-help at r-project.org mailing list
> PLEASE do read the posting guide
> and provide commented, minimal, self-contained,
> reproducible code.
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