similar to: A concrete type I/III Sum of square problem

I am changing the title because this is really about the history of anova, and about strata in analysis of variance. As this kind of question has been arising very frequently, an extended comment may be in order. The ideas, and the sums of squares breakdowns, go back to Fisher; see in particular his "Design of Experiments", first published in 1935. This book is still a good

I've recently run into the problem of using aov with nested factors, and wanting to get the type II and III sums of squares. Normally Anova from the car package would do fine, but it doesn't like having an Error included, so my.aov <-aov(Response ~ Treatment + Error(Treatment:Replicate)) Anova(my.aov, type="II") yields Error in Anova(nested.anova) : no applicable method

Hello all. This may be a trivially simple question to answer, but I'm a little bit stumped with respect to the calculation of the F statistics in nested anovas in R. If I understand correctly, the F statistic for the among-subgroups but within groups hypothesis is calculated as MS_subgroups/MS_error, while the F statistic for the factor is calculated as MS_factor/MS_subgroups (I'm

Hello everybody, I have some questions on ANOVA in general and on ANOVA in R particularly. I am not Statistician, therefore I would be very appreciated if you answer it in a simple way. 1. First of all, more general question. Standard anova() function for lm() or aov() models in R implements Type I sum of squares (sequential), which is not well suited for unbalanced ANOVA. Therefore it is better

Does anyone know if there is a package or other method of reading Genstat files directly into R. Genstat isn't listed in the foreign package. Many thanks, Graham [[alternative HTML version deleted]]

Dear all I was looking at the aov documentation page and came across the following which seems like a contradiction to me: " This provides a wrapper to |lm| for fitting linear models to balanced or unbalanced experimental designs." (I presume 'This' refers to aov) and "|aov| is designed for balanced designs, and the results can be hard to interpret without

Dear list-members I am new to R and a statistics beginner. I really like the ease with which I can extract and manipulate data in R, and would like to use it primarily. I've been learning by checking analyses that have already been run in SAS. In an experiment with Y being a response variable, and group a 2-level between-subject factor, and time a 5-level within-subject factor. 2

Hi all I'm doing some things with a colleague comparing different sorts of models. My colleague has fitted a number of glms in Genstat (which I have never used), while the glm I have been using is only available for R. He has a spreadsheet of fitted means from each of his models obtained from using the Genstat "predict" function. For example, suppose we fit the model of the type

Hello All: I am trying to replicate the results of an example found in Alan Agresti's "Categorical Data Analysis" on pages 267-269. The example is one of a 2 x 2 cross-classification table of ordinal counts: job satisfaction and income. I am able to get Agresti's results for the independence model (G^2 = 12.03 with df = 9) assuming as he does that the data is nominal, but

Dear all, I need to understand a thing in the beheaviour of the two functions aov and anova in the following case involving an analysis of ANOVA with repeated measures: If I use the folowing command I don´t get any problem: >aov1 = aov(response ~ stimulus*condition + Error(subject/(stimulus*condition)), >data=scrd) > summary(aov1) Instead if I try to fit the same model for the

I would like to do a logistic multiple regression on a binary variate, with what Genstat calls an offset, ie a regression variable with the coefficient constrained=1. I'm far from home and have Splus but not R here, but any information from either source would be very welcome. I'd hate to have to go back to using Genstat . . . Thanks, Ted. --------------- Dr E.A. (Ted) Catchpole

Hello, Has anyone written R functions for applying self-controlled case series methods (http://statistics.open.ac.uk/sccs/). In fact only thing needed is to modify glm function to allow absorption of effect. Eg. in Poisson model individual effect is used as factor, but it is considered as nuisance term where parameter estimates are not needed. Could anyone point how absorbing individual

Why would someone use lm and ANOVA (anova(lm(x))) instead of AOV (or the other way around)? The mean squares and sum of squares are the same, but the F values and p-values are slightly different. I am modeling a dependent~independent1*independent2. Thanks, Matt Bridgman [[alternative HTML version deleted]]

Hi all, I search the archive for finding a simple solution for using TukeyHSD with a multistratum aov result (a repeated emasure anova). The Question have been asked but I've found no clear answer. res<-aov(y~Fa*Fb+Error(Subject/(Fa*Fb)) ) I think that the problem is that res is an aovlist object instead of the "aov" object required by TukeyHSD. Is there an easy solution to

HI All: Can anyone give me the formulae/steps for calculating the type III sum of squares for an unbalanced 2-way ANOVA design? Eg. we are looking at 8 treatments x 4 doses, with unequal numbers of replications within the groups. I really need the stepwise calculation, as I would try to put it in my own code (possibly in Visual Basic) to automate the task. Thanks very much. Karth.

Has anyone got the pam_chroot module to successfully work in FreeBSD? I have FreeBSD 5.2-RELEASE installed. I copied the appropriate binaries and libraries into my chroot, I can chroot -u test -g test /home/test /usr/local/bin/bash and it works perfectly. So now I am trying to get the pam module to work. I added session required pam_chroot.so debug into the

Hi, I know that is not possible make a stepwise procedure using REML in R, I can use ML for this. For nested design it may be very dangerous due the difference in variance structure, mainly in a splitplot design. ML make significative variables that REML dont make. I read an article that is made a stepwise procedure using GENSTAT. from article: "Terms were dropped from a model in a

Dear R-help members, I'm trying to run LME model on some behavioral data and need confirmations about what I'm doing... Here's the story... I have some behavioral reaction time (RT) data (participants have to detect dome kind of auditory stimuli). the dependant variable is RT measured in milliseconds. 61 participants were tested separated in 4 age groups (unblanced groups,

I'm looking to analyze a large data set: a within-Ss 2*2*1500 design with 20 Ss. However, aov() gives me an error, reproducible as follows: id = factor(1:20) a = factor(1:2) b = factor(1:2) d = factor(1:1500) temp = expand.grid(id=id, a=a, b=b, d=d) temp$y = rnorm(length(temp[, 1])) #generate some random DV data this_aov = aov( y~a*b*d+Error(id/(a*b*d)) , data=temp ) While yields the

I am new to R. I am going to by one of the following book: 1. William N. Venables and Brian D. Ripley. Modern Applied Statistics with S-Plus. Third Edition. Springer, 1999. ISBN 0-387-98825-4. 2. The Fourth Edition of the book from point 1. 3. `S Programming' by W. N. Venables and B. D. Ripley Springer. ISBN 0-387-98966-8, 2000. I can only by one of the above books. Q1. I have found