Dear R-users,
We expect to develop statistic procedures and environnement for the
computational analysis of our experimental datas. To provide a proof of
concept, we plan to implement a test for a given experiment.
Its design split data into 10 groups (including a control one) with 2
mesures for each (ref at t0 and response at t1). We aim to compare each
group response with control response (group 1) using a multiple comparison
procedure (Dunnett test).
Before achieving this, we have to normalize our data : response values
cannot be compared if base line isn't corrected. Covariance analysis seems
to represent the best way to do this. But how to perform this by using R ?
Actually, we have identify some R functions of interest regarding this
matter (lme(), lm() and glm()).
For example we plan to do as describe :
glm(response~baseline) and then simtest(response_corrected~group,
type="Dunnett", ttype="logical")
If a mixed model seems to better fit our experiment, we have some problems
on using the lme function : lme(response~baseline) returns an error
("Invalid formula for groups").
So :
Are fitted values represent the corrected response ?
Is it relevant to perform these tests in our design ?
And how to use lme in a glm like way ?
If someone could bring us your its precious knowledge to validate our
analytical protocol and to express its point of view on implementation
strategy ?
Best regards.
Alexandre MENICACCI
Bioinformatics - FOURNIER PHARMA
50, rue de Dijon - 21121 Daix - FRANCE
[EMAIL PROTECTED]
tél : 03.80.44.76.17
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