[BioC] Classification

[David martin]

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 at mail.nih.gov>
>>>> wrote:
>>>>
>>>>> On Thu, Jun 23, 2011 at 10:58 AM, David
>>>>> martin<vilanew at 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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>>>>
>>>>
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