Dear nameless, A mixed model seems reasonable for your kind of data. lme() from nlme or lmer() from lme4 are good starting points.
Please note that there is R-sig-mixed-models: a R mailing list dedicated to mixed models. Best regards, ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance Kliniekstraat 25 1070 Anderlecht Belgium + 32 2 525 02 51 + 32 54 43 61 85 thierry.onkel...@inbo.be www.inbo.be To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher The plural of anecdote is not data. ~ Roger Brinner The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. ~ John Tukey -----Oorspronkelijk bericht----- Van: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] Namens rad mac Verzonden: vrijdag 11 mei 2012 12:38 Aan: r-help@r-project.org Onderwerp: [R] Outcome~predictor model evaluation, repeated measurements Dear all, I have simple question regarding how to fit a model (i.e. linear) to the data. Say I have 10 subjects with different phenotypes (dependent var Y, identical for a particular subject) and one predictor variable measured 3 times for each subject (X). By other words: Y Subj X 1 1 1.2 1 1 1.3 1 1 0.7 3 2 2.1 3 2 2.5 3 2 4 5 3 3 5 3 4 5 3 4 ... 20 10 12 20 10 13 20 10 12.5 Subj is a grouping variable. I would like know the correlation of Y with X (Y~X) and the effect of within subject variance on this correlation. And thus, overall significance and correlation. Will it be valid fitting lm to all combinations of x and y and take an average values of p and R-squared? Usually, I estmate the correlation using simple lm between outcome and averaged predictor (1-to-1, i.e. 20 outcomes versus 20 predictors). However, I would like to take in account variations associated with replicated measurements (i.e. the same 20 outcomes versus 20 predictors replicated say 3 times), and, therefore, evaluate slope and intercept variabilities. Do mixed model regression analysis suitable for such an analysis for example using lme function from nlme package? If not, what kind of analysis is most appropriate? Weighted least squares?Thank you. [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. * * * * * * * * * * * * * D I S C L A I M E R * * * * * * * * * * * * * Dit bericht en eventuele bijlagen geven enkel de visie van de schrijver weer en binden het INBO onder geen enkel beding, zolang dit bericht niet bevestigd is door een geldig ondertekend document. The views expressed in this message and any annex are purely those of the writer and may not be regarded as stating an official position of INBO, as long as the message is not confirmed by a duly signed document. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
