# [R] simulating Gaussian Mixture Method

Moshe Olshansky 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
> Hi,
>
>   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
> mixture
> distribution.
>
>   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
> considered
> 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
> wrong???
>
>    thanks so much !
>
>
>
>
> --------------------------
> Peng Jiang
> 江鹏
> Ph.D. Candidate
>
> Antai College of Economics & Management
> 安泰经济管理学院
> Department of Mathematics
> 数学系
> Shanghai Jiaotong University (Minhang Campus)
> 200240 Shanghai
> P. R. China
>
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