[R] Dominant factors in aov?
Hi all, I'm using R 2.0.1. for Windows to analyze the influence of following factors on response Y: A (four levels) B (three levels) C (two levels) D (29 levels) with E (four replicates) The dataset looks like this: A B C D E Y 0 1 1 1 1 491.9 0 1 1 1 2 618.7 0 1 1 1 3 448.2 0 1 1 1 4 632.9 250 1 1 1 1 92.4 250 1 1 1 2 117 250 1 1 1 3 35.5 250 1 1 1 4 102.7 500 1 1 1 1 47 500 1 1 1 2 57.4 500 1 1 1 3 6.5 500 1 1 1 4 50.9 10001 1 1 1 0.7 10001 1 1 2 6.2 10001 1 1 3 0.5 10001 1 1 4 1.1 0 2 2 2 1 6 0 2 2 2 2 4.2 0 2 2 2 3 20.3 0 2 2 2 4 3.5 250 2 2 2 1 8.4 250 2 2 2 2 2.8 etc. If I ask the following: summary(aov(Y~A+B+C+D+E)) R gives me this answer: Df Sum Sq Mean Sq F value Pr(F) A 3 135.602 45.201 310.2166 2e-16 *** B 2 0.553 0.276 1.8976 0.1512 C 1 0.281 0.281 1.9264 0.1659 D25 92.848 3.714 25.4890 2e-16 *** E 3 0.231 0.077 0.5279 0.6634 Residuals 411 59.885 0.146 Can someone explain me why factor C has only 25 Df (in stead of 28, what I expected), and why this number changes when I leave out factors B or C (but not A)? Why do factors B and C (but again: not A) not show up in the calculation if they appear later in the formula than D? When I ask summary.lm(aov(Y~A+B+C+D+E)), R tells me that three levels of D were not defined because of singularities (what does this word mean?). After checking and playing around with the dataset, I find no logical reason for which levels are not defined. Even if I construct a perfect dataset (balanced, no missing values) I never get the correct number of Df. My other datasets are analyzed as expected using the similar function calls and similar datasets. Am I doing something wrong here? Many thanks, René Eschen. ___ drs. René Eschen CABI Bioscience Switzerland Centre 1 Rue des Grillons CH-2800 Delémont Switzerland +41 32 421 48 87 (Direct) +41 32 421 48 70 (Secretary) +41 32 421 48 71 (Fax) http://www.unifr.ch/biol/ecology/muellerschaerer/group/eschen/eschen.html __ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] Dominant factors in aov?
I'm not a statistician, so take what I say with a grain of salt. On 12/02/04 06:29, Rene Eschen wrote: Can someone explain me why factor C has only 25 Df (in stead of 28, what I expected), and why this number changes when I leave out factors B or C (but not A)? Why do factors B and C (but again: not A) not show up in the calculation if they appear later in the formula than D? When I ask summary.lm(aov(Y~A+B+C+D+E)), R tells me that three levels of D were not defined because of singularities (what does this word mean?). After checking and playing around with the dataset, I find no logical reason for which levels are not defined. Even if I construct a perfect dataset (balanced, no missing values) I never get the correct number of Df. I would guess that the factors are somewhat predictable from each other. That is, there is some redundancy. Try predicting each factor from all the others, without the dependent variable. Jon -- Jonathan Baron, Professor of Psychology, University of Pennsylvania Home page: http://www.sas.upenn.edu/~baron R search page: http://finzi.psych.upenn.edu/ __ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] Dominant factors in aov?
Dear Rene, First of all, note that A,B,C,D, and E need to be declared as factors in the beginning, using factor() (but I think you did this already). Also, make sure that the data are read into R in the correct way (i.e. . separating decimal places). The reason for the singularities is that B, C and D are not independent (in fact, they´re identical in their factor levels, and hence in their effect on Y). For this reason, only the effects of A, B and E can be estimated: Df Sum Sq Mean Sq F valuePr(F) A3 302286 100762 7.9887 0.002396 ** B1 422869 422869 33.5263 4.683e-05 *** E3 222817427 0.5888 0.632334 Residuals 14 176583 12613 A has 4 levels so there should be 3 d.f. (that´s correct in the table) B has 2 levels so there is only 1 d.f. (that´s also correct) E has 4 levels so there should be 3 d.f. (also O.K.) In total, there are [(n=22)-(3)-(1)-(3)] -1 = 14 residual d.f., so that´s OK, too. Hope this helps, Christoph levels(A) [1] 0250 500 1000 levels(B) [1] 1 2 levels(E) [1] 1 2 3 4 Rene Eschen wrote: Hi all, I'm using R 2.0.1. for Windows to analyze the influence of following factors on response Y: A (four levels) B (three levels) C (two levels) D (29 levels) with E (four replicates) The dataset looks like this: A B C D E Y 0 1 1 1 1 491.9 0 1 1 1 2 618.7 0 1 1 1 3 448.2 0 1 1 1 4 632.9 250 1 1 1 1 92.4 250 1 1 1 2 117 250 1 1 1 3 35.5 250 1 1 1 4 102.7 500 1 1 1 1 47 500 1 1 1 2 57.4 500 1 1 1 3 6.5 500 1 1 1 4 50.9 10001 1 1 1 0.7 10001 1 1 2 6.2 10001 1 1 3 0.5 10001 1 1 4 1.1 0 2 2 2 1 6 0 2 2 2 2 4.2 0 2 2 2 3 20.3 0 2 2 2 4 3.5 250 2 2 2 1 8.4 250 2 2 2 2 2.8 etc. If I ask the following: summary(aov(Y~A+B+C+D+E)) R gives me this answer: Df Sum Sq Mean Sq F value Pr(F) A 3 135.602 45.201 310.2166 2e-16 *** B 2 0.553 0.276 1.8976 0.1512 C 1 0.281 0.281 1.9264 0.1659 D 25 92.848 3.714 25.4890 2e-16 *** E 3 0.231 0.077 0.5279 0.6634 Residuals 411 59.885 0.146 Can someone explain me why factor C has only 25 Df (in stead of 28, what I expected), and why this number changes when I leave out factors B or C (but not A)? Why do factors B and C (but again: not A) not show up in the calculation if they appear later in the formula than D? When I ask summary.lm(aov(Y~A+B+C+D+E)), R tells me that three levels of D were not defined because of singularities (what does this word mean?). After checking and playing around with the dataset, I find no logical reason for which levels are not defined. Even if I construct a perfect dataset (balanced, no missing values) I never get the correct number of Df. My other datasets are analyzed as expected using the similar function calls and similar datasets. Am I doing something wrong here? Many thanks, René Eschen. ___ drs. René Eschen CABI Bioscience Switzerland Centre 1 Rue des Grillons CH-2800 Delémont Switzerland +41 32 421 48 87 (Direct) +41 32 421 48 70 (Secretary) +41 32 421 48 71 (Fax) http://www.unifr.ch/biol/ecology/muellerschaerer/group/eschen/eschen.html __ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html __ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] Dominant factors in aov?
Dear Rene, At least from the part of the data.frame attached to your mail, I assumed that C,D and E changed in identical ways (but maybe I got this wrong). With your following combination of factors: A (four levels) B (three levels) C (two levels) D (29 levels) with E (four replicates) And assuming independence of the treatment levels, you should get 3 d.f. for A 2 d.f. for B 28 d.f. for D 3 d.f. for E ? residual d.f. (how big is total number of Y values?) The problem arises if parts of treatments B,D and E are applied to the same subjects, e.g. B D E Y 1 1 1 400 2 2 2 300 2 2 3 420 2 2 4 350 (etc) then you immediately run into problems because treatments B and D (in this case) change in an identical way, i.e. the variances calculated for each level of B and D are the same; this is what causes the ´singularities´. Errors need to be independent, otherwise you will have order dependence in your analyses. i.e. the output of your aov model will change depending on the sequence in which the terms A,B,C,D,E are entered. Did I get this right? It would probably help to see the full dataset Best wishes Christoph Rene Eschen wrote: Dear Christoph, The reason for the singularities is that B, C and D are not independent (in fact, they´re identical in their factor levels, and hence in their effect on Y). I do not understand this. You gave the correct levels for A, B and E, but I do not see how they are identical. They have different levels and different codings, or is it because A has the same number of levels as E, and E shares some of the coding with B? René Eschen. --- For this reason, only the effects of A, B and E can be estimated: Df Sum Sq Mean Sq F valuePr(F) A3 302286 100762 7.9887 0.002396 ** B1 422869 422869 33.5263 4.683e-05 *** E3 222817427 0.5888 0.632334 Residuals 14 176583 12613 A has 4 levels so there should be 3 d.f. (that´s correct in the table) B has 2 levels so there is only 1 d.f. (that´s also correct) E has 4 levels so there should be 3 d.f. (also O.K.) In total, there are [(n=22)-(3)-(1)-(3)] -1 = 14 residual d.f., so that´s OK, too. Hope this helps, Christoph levels(A) [1] 0250 500 1000 levels(B) [1] 1 2 levels(E) [1] 1 2 3 4 Rene Eschen wrote: Hi all, I'm using R 2.0.1. for Windows to analyze the influence of following factors on response Y: A (four levels) B (three levels) C (two levels) D (29 levels) with E (four replicates) The dataset looks like this: A B C D E Y 0 1 1 1 1 491.9 0 1 1 1 2 618.7 0 1 1 1 3 448.2 0 1 1 1 4 632.9 250 1 1 1 1 92.4 250 1 1 1 2 117 250 1 1 1 3 35.5 250 1 1 1 4 102.7 500 1 1 1 1 47 500 1 1 1 2 57.4 500 1 1 1 3 6.5 500 1 1 1 4 50.9 10001 1 1 1 0.7 10001 1 1 2 6.2 10001 1 1 3 0.5 10001 1 1 4 1.1 0 2 2 2 1 6 0 2 2 2 2 4.2 0 2 2 2 3 20.3 0 2 2 2 4 3.5 250 2 2 2 1 8.4 250 2 2 2 2 2.8 etc. If I ask the following: summary(aov(Y~A+B+C+D+E)) R gives me this answer: Df Sum Sq Mean Sq F value Pr(F) A 3 135.602 45.201 310.2166 2e-16 *** B 2 0.553 0.276 1.8976 0.1512 C 1 0.281 0.281 1.9264 0.1659 D 25 92.848 3.714 25.4890 2e-16 *** E 3 0.231 0.077 0.5279 0.6634 Residuals 411 59.885 0.146 Can someone explain me why factor C has only 25 Df (in stead of 28, what I expected), and why this number changes when I leave out factors B or C (but not A)? Why do factors B and C (but again: not A) not show up in the calculation if they appear later in the formula than D? When I ask summary.lm(aov(Y~A+B+C+D+E)), R tells me that three levels of D were not defined because of singularities (what does this word mean?). After checking and playing around with the dataset, I find no logical reason for which levels are not defined. Even if I construct a perfect dataset (balanced, no missing values) I never get the correct number of Df. My other datasets are analyzed as expected using the similar function calls and similar datasets. Am I doing something wrong here? Many thanks, René Eschen. ___ drs. René Eschen CABI Bioscience Switzerland Centre 1 Rue des Grillons CH-2800 Delémont Switzerland +41 32 421 48 87 (Direct) +41 32 421
