Re: [R] define F-ratio computations with aov
Many thanks to both of you! I used the summary(obj.aov)$Mean way and I have the correct values now, although slightly different from the analogous SPSS computations. I'll try splplot and Error methods in a calmer period, but I have the feeling that in such cases aov is not the best method anyways and one should proceed to lme4 package. aleXis Από: RICHARD M. HEIBERGER [rmh(at)temple.edu] Αποστολή: Τετάρτη, 17 Μαρτίου 2010 4:21 μμ Προς: Galanidis Alexandros Κοιν.: R Maillist Θέμα: Re: [R] define F-ratio computations with aov Please look at the Error term in ?aov On Wed, Mar 17, 2010 at 4:25 AM, Galanidis Alexandros wrote: Greetings to all, This is my model: aov.fit-aov(Y~A+B+C+D+E+A:C+A:E) In summary(aov.fit) all F values are comptuted by eg MS(A)/MS(Residuals). This is not correct (or what I want), except for F(B) and F(A:E). I suppose P values are not correct either. Is it possible with aov to define the way F computations will be done? I 'd like them to be like this: F(A)=MS(A)/MS(E), F(C)=MS(C)/MS(E), F(D)=MS(D)/MS(E), F(E)=MS(E)/MS(A:E), F(A:C)=MS(A:C)/MS(A:E) For your example, it looks like mydata.aov - aov(Y ~ A+B+C+D + Error(E/A), data=mydata) might be what you need. It isn't possible to be sure of the correct statement for your example without seeing the actual treatment plan. The standard split-plot design is specified with splplot - data.frame(y=rnorm(72), blocks=factor(rep(1:6, each=12)), plots=factor(rep(rep(1:3, each=4), 6)), subplots=factor(rep(1:4, 18)), A=factor(rep(c(3,1,2, 3,1,2, 2,3,1, 3,2,1, 2,1,3, 1,2,3), each=4)), B=factor( c(4, 3, 2, 1, 1, 2, 4, 3, 1, 2, 3, 4, 3, 1, 2, 4, 4, 1, 2, 3, 2, 1, 3, 4, 2, 3, 4, 1, 4, 2, 3, 1, 1, 4, 2, 3, 3, 4, 1, 2, 1, 3, 4, 2, 2, 3, 4, 1, 4, 1, 3, 2, 3, 4, 1, 2, 3, 4, 2, 1, 3, 1, 4, 2, 4, 3, 1, 2, 1, 2, 3, 4))) splplot.aov - aov(y ~ A*B + Error(blocks/plots/subplots), data=splplot) summary(splplot.aov) summary(splplot.aov) Error: blocks Df Sum Sq Mean Sq F value Pr(F) Residuals 5 2.0231 0.40463 Error: blocks:plots Df Sum Sq Mean Sq F value Pr(F) A 2 0.2876 0.1438 0.1135 0.8938 Residuals 10 12.6646 1.2665 Error: blocks:plots:subplots Df Sum Sq Mean Sq F value Pr(F) B 3 2.814 0.93797 0.8987 0.4493 A:B6 3.358 0.55964 0.5362 0.7778 Residuals 45 46.965 1.04367 As you see, the correct F tests are automatically determined and displayed. Rich Από: Michael Rennie [mdrennie(at)gmail.com] Αποστολή: Τετάρτη, 17 Μαρτίου 2010 2:32 μμ Προς: Galanidis Alexandros Κοιν.: R Maillist Θέμα: Re: [R] define F-ratio computations with aov Howdy, In the past, I've just run the ANOVA as normal, and then just grabbed the appropriate MS for the estimation of F ratios. Eg, this will get you the MS in your anova object: summary(obj.aov)[[1]][3] or summary(obj.aov)$Mean And if you want a specific MS, summary(obj.aov)[[1]][[1,3]] or summary(obj.aov)[[1]]$Mean[1] Then you can just put whichever MS over whichever other MS, estimate your F-ratios, with something like: Ffact- summary(obj.aov)[[1]]$Mean[1]/summary(obj.aov)[[1]]$Mean[3] estimate the p-values with: pFfact-1-pf(Ffact, summary(obj.aov)[[1]]$Df[1], summary(obj.aov)[[1]]$Df[3]) and you're off to the races. You can also specify error strata in the aov() model, but then all you get is the MS and you have to estimate your F-ratios anyway (though the indexing is a little different). E.g., if you had a nested anova, you could specify it as: ex.aov-aov(Fixed ~ Nested + Error(Nested/Fixed)) At least this way, the summary() doesn't give you the wrong F-ratios, so you aren't temped to interpret them incorrectly (as you would in the previous example). HTH, Mike __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] define F-ratio computations with aov
Greetings to all, This is my model: aov.fit-aov(Y~A+B+C+D+E+A:C+A:E) In summary(aov.fit) all F values are comptuted by eg MS(A)/MS(Residuals). This is not correct (or what I want), except for F(B) and F(A:E). I suppose P values are not correct either. Is it possible with aov to define the way F computations will be done? I 'd like them to be like this: F(A)=MS(A)/MS(E), F(C)=MS(C)/MS(E), F(D)=MS(D)/MS(E), F(E)=MS(E)/MS(A:E), F(A:C)=MS(A:C)/MS(A:E) thanks __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] define F-ratio computations with aov
Howdy, In the past, I've just run the ANOVA as normal, and then just grabbed the appropriate MS for the estimation of F ratios. Eg, this will get you the MS in your anova object: summary(obj.aov)[[1]][3] or summary(obj.aov)$Mean And if you want a specific MS, summary(obj.aov)[[1]][[1,3]] or summary(obj.aov)[[1]]$Mean[1] Then you can just put whichever MS over whichever other MS, estimate your F-ratios, with something like: Ffact- summary(obj.aov)[[1]]$Mean[1]/summary(obj.aov)[[1]]$Mean[3] estimate the p-values with: pFfact-1-pf(Ffact, summary(obj.aov)[[1]]$Df[1], summary(obj.aov)[[1]]$Df[3]) and you're off to the races. You can also specify error strata in the aov() model, but then all you get is the MS and you have to estimate your F-ratios anyway (though the indexing is a little different). E.g., if you had a nested anova, you could specify it as: ex.aov-aov(Fixed ~ Nested + Error(Nested/Fixed)) At least this way, the summary() doesn't give you the wrong F-ratios, so you aren't temped to interpret them incorrectly (as you would in the previous example). HTH, Mike Galanidis Alexandros wrote: Greetings to all, This is my model: aov.fit-aov(Y~A+B+C+D+E+A:C+A:E) In summary(aov.fit) all F values are comptuted by eg MS(A)/MS(Residuals). This is not correct (or what I want), except for F(B) and F(A:E). I suppose P values are not correct either. Is it possible with aov to define the way F computations will be done? I 'd like them to be like this: F(A)=MS(A)/MS(E), F(C)=MS(C)/MS(E), F(D)=MS(D)/MS(E), F(E)=MS(E)/MS(A:E), F(A:C)=MS(A:C)/MS(A:E) thanks __ 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. __ 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.
Re: [R] define F-ratio computations with aov
Please look at the Error term in ?aov On Wed, Mar 17, 2010 at 4:25 AM, Galanidis Alexandros a...@env.aegean.grwrote: Greetings to all, This is my model: aov.fit-aov(Y~A+B+C+D+E+A:C+A:E) In summary(aov.fit) all F values are comptuted by eg MS(A)/MS(Residuals). This is not correct (or what I want), except for F(B) and F(A:E). I suppose P values are not correct either. Is it possible with aov to define the way F computations will be done? I 'd like them to be like this: F(A)=MS(A)/MS(E), F(C)=MS(C)/MS(E), F(D)=MS(D)/MS(E), F(E)=MS(E)/MS(A:E), F(A:C)=MS(A:C)/MS(A:E) For your example, it looks like mydata.aov - aov(Y ~ A+B+C+D + Error(E/A), data=mydata) might be what you need. It isn't possible to be sure of the correct statement for your example without seeing the actual treatment plan. The standard split-plot design is specified with splplot - data.frame(y=rnorm(72), blocks=factor(rep(1:6, each=12)), plots=factor(rep(rep(1:3, each=4), 6)), subplots=factor(rep(1:4, 18)), A=factor(rep(c(3,1,2, 3,1,2, 2,3,1, 3,2,1, 2,1,3, 1,2,3), each=4)), B=factor( c(4, 3, 2, 1, 1, 2, 4, 3, 1, 2, 3, 4, 3, 1, 2, 4, 4, 1, 2, 3, 2, 1, 3, 4, 2, 3, 4, 1, 4, 2, 3, 1, 1, 4, 2, 3, 3, 4, 1, 2, 1, 3, 4, 2, 2, 3, 4, 1, 4, 1, 3, 2, 3, 4, 1, 2, 3, 4, 2, 1, 3, 1, 4, 2, 4, 3, 1, 2, 1, 2, 3, 4))) splplot.aov - aov(y ~ A*B + Error(blocks/plots/subplots), data=splplot) summary(splplot.aov) summary(splplot.aov) Error: blocks Df Sum Sq Mean Sq F value Pr(F) Residuals 5 2.0231 0.40463 Error: blocks:plots Df Sum Sq Mean Sq F value Pr(F) A 2 0.2876 0.1438 0.1135 0.8938 Residuals 10 12.6646 1.2665 Error: blocks:plots:subplots Df Sum Sq Mean Sq F value Pr(F) B 3 2.814 0.93797 0.8987 0.4493 A:B6 3.358 0.55964 0.5362 0.7778 Residuals 45 46.965 1.04367 As you see, the correct F tests are automatically determined and displayed. Rich [[alternative HTML version deleted]] __ 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.
