Greg McClelland píše v Pá 04. 04. 2014 v 13:22 -0500: > Hi everyone, > > Using the following data, I need to determine if, independent of mass, > the observed response is associated with sex, location, and/or age > > |example<-data.frame(response=c(2.401,2.588,2.293,2.880,2.655,2.830,3.165,2.665,3.126,2.973,1.725,1.889,2.631,1.750,2.271,2.347,2.173,1.962,1.599,2.297,1.894,2.030),mass=c(460,510,500,475,450,470,600,570,480,560,410,370,425,385,400,420,490,450,390,465,360,490), > loc=c("K","K","K","K","K","K","K","K","K","K","R","R","R","R","R","R","R","R","R","R","R","R"),sex=c("F","M","M","F","M","F","M","M","F","M","F","F","M","F","F","F","M","F","F","M","F","F"),| > |age > =c(48,49,47,45,49,49,50,48,49,48,47,48,47,50,48,50,48,50,48,50,50,50))| > > This should be relatively simple but I've been hung up on it for a few > days now and could use a hand. If I understand it correctly, in order to > run an ANCOVA in R I need to set the contrasts from their default > (non-orthogonal) to orthogonal.
> I'm struggling to find the best method > to do this. Admittedly, contrast matrices are a new challenge I have yet > to master which isn't helping. Any assistance would be greatly appreciated. > > Once the orthogonal contrasts are set, I'm guessing everything else is > straightforward (with pkg 'car' installed): > > model.1=aov(response~mass+loc+sex+age,data=example) > Anova(model.1,type="III") > Summary.lm(model.1) > | > Many thanks in advance, > G Hi Greg, AFAIK you do not need to perform such an exercise. On orthogonality: it is great if you have *data* collection design that makes them orthogonal and balanced according to factors of your interest. That means: same count of males and females, same count of individuals per locality, all ages groups in all localities, and all these combined. If data come from observation, this is improbable, but closer to this the better. By default, contrasts in factors (predictors) are set to "treatment" by default in R - and these are orthogonal, too. Orthogonality in this context (I guess/hope someone will correct me) means that R squeezes as much variance per factor level as it could and puts these into comparison. "Treatment contrasts" means: effects shown are differences between particular level mean and mean of the subgroup that comes first in the alphabet (i.e. locality "K", sex "F",...). If this does not suit you, You can change the reference group using "relevel()", specify another way of comparing level means (you can compare them to overall mean, mean of the preceding levels in sequence, ... - "sum" and "helmert" contrast are built in) or by setting your own contrast matrix, which comes handy if you wish e.g. to compare treatments pairwise. In short: run model.1=aov(response~mass+loc+sex+age,data=example) or model.1=aov(response~mass*loc*sex*age,data=example) or something in between. In M. Crawley's "R book", look at the Chapter 12: Analysis of covariance and Sub-chapters of ch. 9: "Statisticall modelling" - 9.22 and 9.23 (Here I reference second edition). HTH. Martin > > [[alternative HTML version deleted]] > > _______________________________________________ > R-sig-ecology mailing list > R-sig-ecology@r-project.org > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology -- ------------------------------ Pokud je tento e-mail součástí obchodního jednání, Přírodovědecká fakulta Univerzity Karlovy v Praze: a) si vyhrazuje právo jednání kdykoliv ukončit a to i bez uvedení důvodu, b) stanovuje, že smlouva musí mít písemnou formu, c) vylučuje přijetí nabídky s dodatkem či odchylkou, d) stanovuje, že smlouva je uzavřena teprve výslovným dosažením shody na všech náležitostech smlouvy. _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology