You have an ordinal response, so you might consider an ordered probit model
with interaction terms and a penalized likelihood fit, and determine the
best penalty by cross-validation.  I don't recall whether CMA supports
ordered probit models, but it's probably the best approach, and you could
just brute-force it -- you've only got 120 different models to fit under
this scheme.  At the very least, CMA would generate the cross-validation
sets for you.

You might also want to consider recursively fitting a shrunken LDA model
(diseased/healthy, moderate/severe) and see how that compares to an ordinal
model.  Regardless, cross-validation is the obvious answer to how to pick
one.

Hope this helps,
-t

On Fri, Jun 24, 2011 at 8:24 AM, David martin <vilanew@gmail.com> wrote:

> thanks.
> Is not binary since i have three categories and 5 genes. I have tried LDA
> and stepclass
>
> #LDR stepwise
> disc<-stepclass(Group~ ., data =dataf, method = "lda",improvement = 0.001)
>
> where group contains my three categories ("healthy","moderate disease",
> "severe disease") and dataf the pcr values for my 5 genes.
>
> The problem i have is that stepwise generates a different signature each
> time (as it randomly picks up a gene to start with)? This is ok for me but
> how many times do you need to run stepclass so that you found your mopst
> probable genes that classify your groups , Do i need to do a loop for
> stepclass ???
>
> thanks
>
>
>
> On 06/24/2011 05:17 PM, Kevin R. Coombes wrote:
>
>> .. and probably should ...
>>
>> For a binary classification with only a few predictors, you can, for
>> example, use logistic regression with some standard criterion like AIC,
>> BIC, or Bayesian model averaging to decide which predictors should be
>> retained.
>>
>> Kevin
>>
>> On 6/23/2011 6:10 PM, Moshe Olshansky wrote:
>>
>>> If you have just 5 genes and a decent number of samples you can use
>>> any of
>>> the "conventional" (i.e. not high throughput) methods like LDA, trees,
>>> Random Forest, SVM, etc.
>>>
>>>  I will have a look at both packages. It's pcr data by the way
>>>> thanks
>>>>
>>>> On 06/23/2011 05:56 PM, Tim Triche, Jr. wrote:
>>>>
>>>>> or CMA, which is perhaps a more systematic approach for classification.
>>>>> (the package name stands for Classification of MicroArrays) Very well
>>>>> thought out.
>>>>>
>>>>>
>>>>> On Thu, Jun 23, 2011 at 8:02 AM, Sean
>>>>> Davis<sdavis2@mail.nih.gov>
>>>>> wrote:
>>>>>
>>>>>  On Thu, Jun 23, 2011 at 10:58 AM, David
>>>>>> martin<vilanew@gmail.com>
>>>>>> wrote:
>>>>>>
>>>>>>> Hi,
>>>>>>> I have 5 genes of interest. I would like to know which combination(s)
>>>>>>> of
>>>>>>> genes gives the best disease separation. Which test could i use in my
>>>>>>> training set to see which combination is the best classificer between
>>>>>>> my
>>>>>>> disease and my healthy population.
>>>>>>>
>>>>>>> Thanks for any comment or test that could be useful to answer that
>>>>>>>
>>>>>> question.
>>>>>>
>>>>>> Check out the MLInterfaces package. It should give you some ideas on
>>>>>> where to start.
>>>>>>
>>>>>> Sean
>>>>>>
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