Dear Dave, there are some inconsistencies in your explanation of the problem. You said your variables are:
> CO is a continuous response variable, > > Week is a fixed categorical factor, > > Habitat is a fixed categorical factor, and > > Location is a random categorical factor nested within Habitat. What does this last statement mean? Are the Locations identified with the same names in both Habitats (e.g. Location=1,2,3,... for Habitat=Control, and then Location=1,2,3... for Habitat=Treatment, although the Locations of both Habitats have nothing to do with each other? Or do all 13 Locations receive different names? If Location is really nested within Habitat, you would be in the former case, and then your random terms should include the interaction "Location:Habitat". In the latter case, the random term would just be "Location". But then, your model with aov is: > mCO = aov(CO ~ Week * Habitat + Error(Location/Week)) Since you don't include Habitat in the Error term, I would say that Location is not really nested within Habitat. But then, why is Week nested within Location? Do you mean that the effect of Week may be affected by the random Location? Anyway, your interpretation of the ANOVA table is misleading: > Error: Location > > Df Sum Sq Mean Sq F value Pr(>F) > > Habitat 1 182566 182566 8.6519 0.01341 * > > Residuals 11 232115 21101 > > > > Error: Location:Week > > Df Sum Sq Mean Sq F value Pr(>F) > > Week 10 596431 59643 11.0534 7.5e-13 *** > > Week:Habitat 10 196349 19635 3.6389 0.0003251 *** > > Residuals 110 593551 5396 This actually means: For the F test of Habitat, the denominator MS is that for Location For the F test of Week, the denominator MS is that for the Location x Week For the F test of Habitat x Week, the denominator MS is that for Location x Week And then, you wrote your attempt with lmer: > m. = lmer(CO ~ Week * Habitat + (1|Habitat/Location)) The random term here (1|Habitat/Location) has nothing to do with the Error term you used in aov. If location is really nested within Habitat, perhaps you meant m. = lmer(CO ~ Week * Habitat + (1|Habitat:Location)) (Habitat/Location means that Habitat has a random effect per se as well, and I guess you don't mean that!) Or if Location is not really nested, m. = lmer(CO ~ Week * Habitat + (1|Location)) or if you really wanted the same model as with aov: m. = lmer(CO ~ Week * Habitat + (1|Location/Week)) Please clarify your model. Otherwise it would be impossible to make any comparison. Helios >>> El día 29/09/2011 a las 6:30, Dave Robichaud <drobich...@lgl.com> escribió: > Hi All, > > I am frustrated by mixed-effects model! I have searched the web for > hours, and found lots on the nested anova, but nothing useful on my > specific case, which is: a random factor (C) is nested within one of the > fixed-factors (A), and a second fixed factor (B) is crossed with the > first fixed factor: > > C/A > > A > > B > > A x B > > My question: I have a functioning model using the aov command (see > below), and I would now would like to recode it, using a more flexible > command such as lme or lmer. Once I have the equivalent syntax down, I > would ideally like to re-run my analysis using "family = poisson", as CO > is actually count data. > > I have a dataset including a response variable CO, measured once per > Week (for 11 weeks) at 13 Locations. The 13 Locations are divided into 2 > habitat types (Control and Treatment). > > Thus: > > CO is a continuous response variable, > > Week is a fixed categorical factor, > > Habitat is a fixed categorical factor, and > > Location is a random categorical factor nested within Habitat. > > Here is my model in R: > > mCO = aov(CO ~ Week * Habitat + Error(Location/Week)) > > summary(mCO) > > And the output: > > Error: Location > > Df Sum Sq Mean Sq F value Pr(>F) > > Habitat 1 182566 182566 8.6519 0.01341 * > > Residuals 11 232115 21101 > > > > Error: Location:Week > > Df Sum Sq Mean Sq F value Pr(>F) > > Week 10 596431 59643 11.0534 7.5e-13 *** > > Week:Habitat 10 196349 19635 3.6389 0.0003251 *** > > Residuals 110 593551 5396 > > Given that this is a mixed model, I believe the appropriate error terms > are as follows: > > For the F test of Habitat, the denominator MS is that for location/habitat; > > For the F test of Week, the denominator MS is the residual; and > > For the F test of Habitat x Week, the denominator MS is the residual. > > My tinkering with lmer and lme have not produced results similar to the > above > > For example, > > m. = lmer(CO ~ Week * Habitat + (1|Habitat/Location)) > > anova(m.) > > produces: > > Analysis of Variance Table > > Df Sum Sq Mean Sq F value > > Week 10 596431 59643 11.0534 > > Habitat 1 28652 28652 5.3100 > > Week:Habitat 10 196349 19635 3.6389 > > Any coding advice would be greatly appreciated! > > Thanks for your consideration, > > Dave Robichaud > > > [[alternative HTML version deleted]] INSTITUTO DE BIOMECÁNICA DE VALENCIA Universidad Politécnica de Valencia • Edificio 9C Camino de Vera s/n • 46022 VALENCIA (ESPAÑA) Tel. +34 96 387 91 60 • Fax +34 96 387 91 69 www.ibv.org Antes de imprimir este e-mail piense bien si es necesario hacerlo. 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