[BioC] edgeR: GLM & residuals and model fitting & hypothesis testing

[Susanne Franssen]

Dear Gordon,

thanks, a lot for you answer, however I am a little more uncertain now
what to do.

> 1) You may know that there are many different definitions of "residuals"
> from a generalized linear models, and none of them have the properties of
> residuals from normal linear models.  I have not found residuals very useful
> myself in a genomic differential expression context.
>
> What sort of residuals did you want, and what were you planning to do with
> them?

Indeed I am not aware of this difference!
I was thinking of the residual errors in a normal linear model. The
problem in my analysis is that I only have 3 df (4 libs) to start
with, so I cannot model the complete model
individual+treatment+individual*treatment. So an idea was to model
individual+treatment and afterwards do a model on the interaction only
on the remaining residual errors as new observed vector.
Is something like this at all possible or am I completely wrong here?

> 2) You must always fit the model with all relevant factors:
> individual*treatment.  You cannot do meaningful inference from a reduced
> model until you have determined that the other factors are not important.
> Hence you have to deal with the interaction first.

It makes sense to have to put in all relevent factors to the model.
However, concerning this I don't understand your comment - I have to
deal with the interaction first, wouldn't the full model be:
individual+treatment+individual*treatment (which I can't do because of
a lack of df)
Cosidering my samples & factors:
>> I have a fully crossed design with 2 factors and 2 factor levels each:
>> individual <- as.factor(c("indA","indA","indB","indB"))
>> treatment <- as.factor(c("treat1","treat2","treat1","treat2"))

how would the cmd for the design matrix look like:
design <- model.matrix(~??)
and with which cmd would I fit the model and test for it:
fit <- glmFit(d.GLM,design)
lrt <- glmLRT(d.GLM,fit, coeff=?)

Thanks a lot,
Susanne



>
> Best wishes
> Godon
>
>> Date: Tue, 14 Feb 2012 17:44:31 +0100
>> From: Susanne Franssen <s.franssen at uni-muenster.de>
>> To: bioconductor at r-project.org
>> Subject: [BioC] edgeR: GLM & residuals and model fitting & hypothesis
>>        testing
>>
>> Dear all,
>>
>>
>> 1) GLM & residuals:
>>
>> I have a question concerning the use of GLMs in edgeR and the analysis
>> of the residuals after model fitting.
>>
>> I have followed all the steps until model fitting, e.g.:
>> glmfit.D <- glmFit(D, design, dispersion = D$tagwise.dispersion)
>>
>> The results I obtain from the fitting are the following catgories:
>>>
>>> names(glmfit.D)
>>
>> [1] "coefficients"  "fitted.values" "fail"          "not.converged"
>> [5] "deviance"      "df.residual"   "abundance"     "design"
>> [9] "offset"        "dispersion"    "method"        "counts"
>> [13] "samples"
>>
>> What would be the best way to obtain the residuals for the "genewise"
>> GLMs?
>>
>>
>>
>> 2) model fitting & hypothesis testing:
>>
>> I have a fully crossed design with 2 factors and 2 factor levels each:
>> individual <- as.factor(c("indA","indA","indB","indB"))
>> treatment <- as.factor(c("treat1","treat2","treat1","treat2"))
>>
>> in general I would be interested in 3 different aspects:
>> a) effect of individual
>> b) effect of treatment
>> c) interaction between individual and treatment
>>
>> What would be the best way to test for those effects, would I rather
>> test for all three aspects individually, i.e.:
>> a) design <- model.matrix(~individual)
>> b) design <- model.matrix(~treatment)
>> c) design <- model.matrix(~individual*treatment)
>>
>> or doesn't it also make sense to model
>> design <- model.matrix(~individual+treatment)
>> and test for
>> a) lrt.cd_ind <- glmLRT(D, glmfit.D, coef=2)
>> b) lrt.cd_treat <- glmLRT(D, glmfit.D, coef=3)
>> ... this way the effect of both factors could be accounted for in the
>> model?!
>>
>>
>> Thanks a lot!
>> Susanne
>>
>
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