Dear Bill Venables, Does this mean that in a conventional aov object, the "summary.lm" gives the parameter estimates and p-values computed "marginally", while the "summary.aov" table by default gives sequential sums of squares?
This question arose recently when a colleague and I were discussing differences in the P-values from a model that contained only linear (1 d.f.) terms. Thanks a lot for your assistance, Christoph. [EMAIL PROTECTED] schrieb: > Using anova with the default setting generates a sequential analysis of > variance table. You can see this by noting that if you change the order > of terms in the model, it gives you a different result: > >> incub.lme2 <- lme(egg.temp ~ kjday + treat, random = ~1|ind, data = > incub.df) >> incub.lme3 <- lme(egg.temp ~ treat + kjday, random = ~1|ind, data = > incub.df) >> anova(incub.lme2) > numDF denDF F-value p-value > (Intercept) 1 11 1176.6677 <.0001 > kjday 1 7 5.7060 0.0483 > treat 1 7 9.6364 0.0172 >> anova(incub.lme3) > numDF denDF F-value p-value > (Intercept) 1 11 1176.6677 <.0001 > treat 1 7 14.8398 0.0063 > kjday 1 7 0.5026 0.5013 > > So this is adressing the question of what the additional contribution of > each term is if you add them to the model one after the other. If you > look at kjday *before* you consider the effect of treat, it looks very > significant, but if you allow for the effect of treat and then consider > the additional contribution of kjday, it looks unnecessary. This is a > sure sign that treat and kjday (at least) are partially confounded in > the data, and if you look closely at the data itself you can see this. > > What you were expecting is an anova output which is not additive, but > considers the contribution of each term separately, with all other terms > in the model. This is called a "marginal" anova table, for which you > can ask: > >> anova(incub.lme3, type = "marginal") > numDF denDF F-value p-value > (Intercept) 1 11 21.664654 0.0007 > treat 1 7 9.636384 0.0172 > kjday 1 7 0.502597 0.5013 > > Notice that dropterm(...) from the MASS library can be used for the same > kind of table for simpler LM and GLM models. > > Bill Venables. > > -----Original Message----- > From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] > On Behalf Of Andreas Nord > Sent: Thursday, 1 November 2007 5:17 PM > To: r-help@r-project.org > Subject: [R] F distribution from lme()? > > > Dear all, > > Using the data set and code below, I am interested in modelling how egg > temperature (egg.temp) > is related to energy expenditure (kjday) and clutch size (treat) in > incubating birds using the > lme-function. I wish to generate the F-distribution for my model, and > have > tried to do so using > the anova()-function. However, in the resulting anova-table, the > parameter > kjday has gone from > being highly non-signiicant in the lme-expression, to all of a sudden > being > significant at the > 0.05 level. At the same time, "treat" retains its original p-value. I've > tried to understand why, > but can't really figure it out. So, what has happened and why? How to > best > interpret it? > > Further, any advice on how to best generate F-distributions from the > lme-function is most appreciated. > > Many thanks in advance, > Andreas Nord > Sweden > > > ind treat egg.temp kjday > 79 2 27.33241 42.048 > 42 2 30.73269 41.760 > 10 2 29.54986 38.304 > 206 2 31.78947 45.216 > 23 2 29.69114 40.896 > 24 2 36.48199 46.944 > 45 2 29.76454 44.064 > 29 2 30.56510 42.912 > 78 2 27.71468 43.200 > 79 3 25.88227 45.216 > 42 3 30.95983 44.640 > 10 3 28.13296 45.216 > 206 3 31.77147 45.216 > 23 3 27.50000 42.336 > 5 3 28.16205 51.264 > 24 3 34.69391 48.960 > 45 3 28.79778 46.368 > 368 3 26.18006 45.792 > 29 3 29.75208 45.216 > 78 3 25.28393 43.200 > 44 3 23.32825 44.640 > > > # lme-model with "individual" as random factor >> incub.lme2<-lme(egg.temp~kjday+treat,random=~1|ind,data=incub.df) > > Fixed effects: egg.temp ~ kjday + treat > Value Std.Error DF t-value p-value > (Intercept) 24.937897 6.662475 11 3.743038 0.0032 > kjday 0.108143 0.152540 7 0.708945 0.5013 > treat3 -1.506605 0.485336 7 -3.104254 0.0172 > > > #generating an anova table to get the F-distribution >> anova(incub.lme2) > numDF denDF F-value p-value > (Intercept) 1 11 1176.6686 <.0001 > kjday 1 7 5.7060 0.0483 > treat 1 7 9.6364 0.0172 ______________________________________________ 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.