[R] Use of lme() function

[a.menicacci@fr.fournierpharma.com]

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 at fr.fournierpharma.com
tél : 03.80.44.76.17