Thnx a lot,

It was quite helpful.

Dimitris

2011/6/30 James W. MacDonald <jmacdon@med.umich.edu>

> Hi Dimitris,
>
>
> On 6/30/2011 3:24 PM, Dimitris Kampas wrote:
>
>> Hello everyone,
>>
>> I would like to perfom a linear model like the following on my data using
>> limma but i am not familiar with that.
>>
>> Y~ a + b*TYPE + c*TIME + d * TYPE * TIME
>> I have 2 types of mice, CSB and WT, and 3 time points.
>> I would like to model the time as a continous covariate.
>> Unfortunately, the limma examples i found contain only categrical
>> covariates.
>> Can anybody provide me any help? Or does anybody know where to find an
>> example of limma model with
>> continous covariate.
>>
>
> You don't say what kind of data you are analyzing, so I will keep this
> pretty general. What you need to understand is that the design matrix set up
> by R is dependent on the class of the input data. So if you pass numerical
> data to model.matrix(), then R will assume you want to model those data as a
> continuous variable. For example,
>
> > z <- 1:4
>
> Fit z as a continuous variable:
>
> > model.matrix(~z)
>  (Intercept) z
> 1           1 1
> 2           1 2
> 3           1 3
> 4           1 4
> attr(,"assign")
> [1] 0 1
>
> Fit z as factor levels:
>
> > model.matrix(~factor(z))
>  (Intercept) factor(z)2 factor(z)3 factor(z)4
> 1           1          0          0          0
> 2           1          1          0          0
> 3           1          0          1          0
> 4           1          0          0          1
> attr(,"assign")
> [1] 0 1 1 1
> attr(,"contrasts")
> attr(,"contrasts")$`factor(z)`
> [1] "contr.treatment"
>
>
> Does that help?
>
> Best,
>
> Jim
>
>
>
>> Dimitris Kampas
>>
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>>
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>
> --
> James W. MacDonald, M.S.
> Biostatistician
> Douglas Lab
> University of Michigan
> Department of Human Genetics
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