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Posted 11-06-2018 07:02 AM
(1323 views)

in case of multiple variables(+100 variables),the following statement is OK?

proc fmm data=XXX;

class XXX;←think separately about model?

model A=,B=C=,・・・・・・ = / k=X;

run;

2 REPLIES 2

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Your statements are not valid syntax.

1. How does the classification variable enter the models?

2. The K= option requires a constant value, such as K=2 or K=3.

3. You cannot use commas to separate models. You cannot put multiple models on one MODEL statement.

It is not clear what you are trying to accomplish, but here is a guess.

A. It looks like you might have many response variables named Y1, Y2, Y3, ..., Y100

B. It looks like you are trying to fit them all with a mixture of k Gaussian distributions.

If those assumptions are correct then you should convert your data from wide to long format. Let Y be the name of the long variable that contains the values for variables Y1, ..., Y100 and let B be an indicator variable that indicates which observations correspond to which variables. Then you can use syntax such as the following:

```
proc fmm data=LongData plots=none;
by B;
model Y= / k=2;
run;
```

Almost surely you do not want 100 pages of output, so you should suppress ODS output and write the results (parameter estimates?) to a data set. You should study the techniques in the following articles:

Simulation in SAS: The slow way or the BY way

Turn off ODS when running simulations in SAS

An easy way to run thousands of regressions in SAS

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thank you for replying.

>>It is not clear what you are trying to accomplish, but here is a guess.

A. It looks like you might have many response variables named Y1, Y2, Y3, ..., Y100

B. It looks like you are trying to fit them all with a mixture of k Gaussian distributions.

A and B is right, but I want to cluster combination of multiple variables as well as k-means.

proc fastclus data=stan out=clust maxclusters=3; var v1 v2 v3; run;

I think the way you taught me is to cluster variables separately. Is it right?

if you are OK,please tell me how shoud I wright the statement Gaussian mixture model compared with k-means.

thank you in advance.

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