Hi Thanks for you reply. I wonder if you can help me further.
During my degree, I was taught that a factor should be specified as random if it represents a sample of the total population. So in my case, I need to test the factor genotype to see if genotypes are significantly different from each other (I work on trees that can be clonally replicated and so have biological replication at the level of genotype). As genotypes in my experiment represent only a sample of the total possible number of possible genotypes (which is effectively infinite), I was taught to specify genotype as a random factor (but it is a factor that I am explicitly interested in). In the statistics package I have always used before (Minitab), you specify your model (so maybe height~treatment:genotype to get main factor effects and interaction) and then specify genotype as a random factor and you get an answer. If I read about the calculation of F for mixed models, the main difference seems to be the denominator used. Here is an example from a stats book for the case of a mixed model (type III) with Factor A as fixed and Factor B as random Factor A - factor A MS/AxB MS Factor B - factor B MS/error MS AxB interaction - AxB MS/error MS So I could do that manually in R to recreate the same result that I get in Minitab (but this just recreates the black box without me understanding what the issue is). I think to some extent I might be getting confused because the examples of ANOVA calculation using lme in R concentrate on testing linear models and I was taught ANOVA purely in a SS and MS context with no reference to linear regression and I am finding it hard to think of my requirements in relation to testing the intercept and slope etc (obviously this is entirely my own limitation but any help would be great as I don't have a mathematical background but clearly need to learn some maths). What I need to know from the test is whether treatment has an effect, whether genotypes differ from each other and whether the difference between genotypes is dependant on treatment (i.e. the interaction term). In some cases, I also want to know if the replicates of a genotype differ from each other (so replicate would be nested in genotype). Normally I would presume that replicates of a genotype are just an indication of noise but in some cases I specifically want to know if the lack of a treatment or genotype effect is due to the fact that genotype replicates are highly variable (which would be an indication of phenotypic plasticity). Can you tell me if my thinking that genotype should be a random factor is a mistake on my part or if not, how to specify a model for treatment and genotype with genotype as random and treatment as fixed and then how to get the significance for both factors? Thanks again Nat Street PS I use SS and MS for sum of squares and mean squares. [EMAIL PROTECTED] wrote: > > Nathaniel, > > If you are interested in the particular subject, you should consider them > as a fixed effect, which wil give you what you want. > > If your subjects are really random, the only thing you could be interested > in, is whether considering the subjects as a grouping is helping you in > improving your model. The logical way is to compare two models, one with > and one without Subject, and compare their loglikelihood with the usual > anova() function. > > Joris > > > > > > > > "Nathaniel > Street" > <nathaniel.street To > @plantphys.umu.se r-help@r-project.org > > cc > Sent by: > [EMAIL PROTECTED] Subject > project.org [R] significance for a random > effect in Mixed Model ANOVA > > 14/10/2007 23:48 > > > Please respond to > nathaniel.street@ > plantphys.umu.se > > > > > > > In a number of cases I want to use mixed-model ANOVA tests where I am > interested in whether both the fixed and random effects (and their > interactions) are significant. > > If I use this example > >> library(nlme) >> data(Orthodont) >> anova(lme(distance ~ age + Sex, data = Orthodont, random = ~ 1)) > > I get the result > > numDF denDF F-value p-value > (Intercept) 1 80 4123.156 <.0001 > age 1 80 114.838 <.0001 > Sex 1 25 9.292 0.0054 > > How do I also get a significance value for the random factor (Subject)? > > Incidentally, why does it seem that people are not generally interested in > whether the random variables are different from each other? In the case of > the Orthodont data (if there was replication at the Subject level i.e. if > you could clone humans [as you can plants]), would it not be interesting > to know if subjects (nested within sex) are different to each other as > well as > if there is an age effect (so to know if underlying genotype is also an > important factor)? > > Thanks > > Nat Street > -- > Nathaniel Street > Umeå Plant Science Centre > Department of Plant Physiology > University of Umeå > SE-901 87 Umeå > SWEDEN > > email: [EMAIL PROTECTED] > tel: +46-90-786 5477 > fax: +46-90-786 6676 > www.upsc.se > http://www.citeulike.org/user/natstreet > > ______________________________________________ > 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. > > > > -- Nathaniel Street Umeå Plant Science Centre Department of Plant Physiology University of Umeå SE-901 87 Umeå SWEDEN email: [EMAIL PROTECTED] tel: +46-90-786 5477 fax: +46-90-786 6676 www.upsc.se http://www.citeulike.org/user/natstreet ______________________________________________ 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.
