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I am currently writing a paper in which I have the pleasure to conduct both between and within subjects comparisons. After discussion with my supervisor we decided to run t-tests and use the pretty simple Holm-Bonferroni method (wikipedia) for correcting for alpha error cumulation. It controls for familwise error rate but has a greater power than the ...

Try: library(ez) ezANOVA(data=subset(p12bl, exps==1), within=.(sucrose, citral), wid=.(subject), dv=.(resp) ) and the equivalent aov command, minus sphericity etc, is: aov(resp ~ sucrose*citral + Error(subject/(sucrose*citral)), data= subset(p12bl, exps==1)) Here's the equivalent using Anova from car directly: library(car) ...

Having several repeated-measures DVs one can apply a univariate approach (also called Repeated Measures sensu stricto or split-plot approach) or multivariate approach (or MANOVA). In univariate approach, RM levels are treated as deviations from one variable, their average level. In multivariate approach, RM levels are treated as covariates of each other. ...

Did you try the car package, from John Fox? It includes the function Anova() which is very useful when working with experimental designs. It should give you corrected p-value following Greenhouse-Geisser correction and Huynh-Feldt correction. I can post a quick R example if you wonder how to use it. Also, there is a nice tutorial on the use of R with ...

I generally recommend avoiding these types of sphericity tests altogether by using modern mixed modeling methods. If you are not working with few subjects this will give you a great deal of flexibility in modeling an appropriate covariance structure, freeing you from the strict assumption of sphericity when necessary. I infer from the str output that you ...

If there were an error in Anova it would also be in ezANOVA because Mike Lawrence built ez on John Fox's Anova package. And the Anova package in turn uses the standard R anova command. R is getting this correct. Peter Dalgaard (pp.3-4) pointed out that SAS and SPSS get the H-F calculation wrong, and have for decades. It's because they use the original ...

A couple of points - too long for a comment: If you are protecting your pre-planned tests with Bonferroni correction, than there is no need to run the original ANOVA. The "double protection" only looses power. Most of the standard "post-hoc" tests for ANOVA do not need the protection of the original F-test. Essentially the only approach that needs it is ...

Regarding Bonferroni (and multiple comparisons issues in general) Jacob Cohen, in his book on regression, said "this is a subject on which reasonable people can differ". There are arguments for not doing such corrections at all (see, e.g., this piece by Andrew Gelman). I find such arguments persuasive. If you reduce chance of type 1 error then (other ...

Have a look at the multcomp-package and its vignette Simultaneous Inference in General Parametric Models. I think it should do what wan't and the vignette has very good examples and extensive references.

I prefer a repeated measures model. Not only is it easier to interpret the results, it is more flexible in that you can specify a covariance structure. This reference may be of use as it works through an example: Mixed or MANOVA

I recall some discussion on this in the past; I'm not aware of any implementation of Maxwell & Delaney's approach, although it shouldn't be too difficult to do. Have a look at "Repeated Measures ANOVA using R" which also shows one method of addressing the sphericity issue in Tukey's HSD. You might also find this description of Friedman's test of ...

You have explicitly asked for type-1 computations, which skip the use of car::Anova and therefore miss out on its computation of assumption tests. If your data are balanced with regards to the between-Ss variable, then you can omit the type=1 argument and receive the same results plus assumption tests. If your data aren't balanced with regards to the ...

so... here is a bit of a dog's breakfast of suggestions There are more ways to approach this than the options you give yourself. One of them might be to take your three reward levels, one being neutral, and turn them into two reward effects. So, if C is neutral reward, and A and B are test levels makes up an A effect (A-C) and a B effect (B-C) and then ...