[R] Use of lme() function

From: <a.menicacci_at_fr.fournierpharma.com>
Date: Mon 05 Dec 2005 - 23:49:26 EST

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
a.menicacci@fr.fournierpharma.com

tÚl : 03.80.44.76.17



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