Thanks Gavin, I supposed this to be the problem, but do you know how to 
deal with this situation? I mean, how can I disscard genes which are not 
contributing or contributing in the same manner (lineal-dependent)? I 
finally would like to have an equation which models my data.
Thanks, Paul

Gavin Simpson wrote:
> On Mon, 2009-06-15 at 12:46 +0200, poleteiep34 wrote:
>   
>> Hi all!
>> Maybe someone could help me with the following. I know this hasn't
>> directly to do with ecology but I'm also using glm.
>>  
>> I have a list of 16 genes and 10 samples. The samples are of two
>> types, 4 Ctrl and 6 Diseased. If,
>>
>> labelInd<-as.factor(c(rep("0",4),rep("1",6)))
>> genes.glm<-glm(labelInd ~ ., family=binomial, data=mat)
>>
>>
>> beeing "mat" the 10x16 matrix (without NAs), I got 17 values, first
>> the intercept, 9 numerical values and "NA" for the last 7 genes. Does
>> anybody you know why this is happening or how I can model using the 16
>> genes?
>>     
>
> You don't have enough observations to use all 16 predictors plus
> intercept, if you really only have 10 samples. The NA coefficients are
> the one that can't be estimated given your observations.
>
> You'll need to rethink how to analyse these data.
>
> G
>
>   
>> I hope anyone could help me with this!
>> Many thanks in advance,
>>
>> Paul
>>
>>
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