Re: [R] Anova in 'car': SSPE apparently deficient rank
Colleen F. Moore wrote: I have design with two repeated-measures factor, and no grouping factor. I can analyze the dataset successfully in other software, including my legacy DOS version BMDP, and R's 'aov' function. I would like to use 'Anova' in 'car' in order to obtain the sphericity tests and the H-F corrected p-values. I do not believe the data are truly deficient in rank. I followed the methods for this kind of analysis outlined in Bennett's excellent handouts for his Psychology 710 course http://www.psychology.mcmaster.ca/bennett/psy710/lectures/maxwell_chp12.pdf I am trying to convert my own similar course to R for my students for next fall. I have been successful at analyzing a segment of the data as a 2-way repeated measures design. Here is my code: your.data=read.table(pipe(pbpaste),header=T) your.data partic A1B1 A1B2 A1B3 A1B4 A2B1 A2B2 A2B3 A2B4 A3B1 A3B2 A3B3 A3B4 1 p111231247137 10 2 p2223322562469 3 p3122323261479 4 p411221236238 10 5 p5223323572379 attach(your.data) multmodel=lm(cbind(A1B1, A1B2, A1B3, A1B4, A2B1, A2B2, A2B3, A2B4, A3B1, A3B2, A3B3, A3B4)~1) poke.idata=read.table(pipe(pbpaste),header=T) poke.idata Afac Bfac 1A1 B1 2A1 B2 3A1 B3 4A1 B4 5A2 B1 6A2 B2 7A2 B3 8A2 B4 9A3 B1 10 A3 B2 11 A3 B3 12 A3 B4 attach(poke.idata) pokeAnova =Anova(multmodel,idata=poke.idata,idesign=~Afac*Bfac,type=III) Error in linear.hypothesis.mlm(mod, hyp.matrix, SSPE = SSPE, idata = idata, : The error SSP matrix is apparently of deficient rank = 4 6 Thanks for any help or advice. And thanks for the 'car' package, which is a great asset to my course. I'm just stuck on this one example. Hmm, this does seem to work with regular anova.mlm: anova(multmodel, idata=poke.idata, X=~Afac+Bfac,test=Sph) Analysis of Variance Table Contrasts orthogonal to ~Afac + Bfac Greenhouse-Geisser epsilon: 0.2880 Huynh-Feldt epsilon:0.4871 Df F num Df den DfPr(F) G-G Pr H-F Pr (Intercept) 1 36.67 6 24 6.164e-11 2.5249e-04 3.3530e-06 Residuals4 As far as I recall, the epsilon corrections do not have a formal requirement of a nonsingular SSD of the relevant contrast. Not sure about the accuracy of the F probabilities in such cases, though. -- O__ Peter Dalgaard Øster Farimagsgade 5, Entr.B c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~ - (p.dalga...@biostat.ku.dk) FAX: (+45) 35327907 __ 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] Anova in 'car': SSPE apparently deficient rank
Dear Peter and Colleen, I think that Peter realizes this, but what Anova() does in this case is equivalent to the MANOVA anova(multmodel, M = ~ Afac*Bfac, X = ~Afac + Bfac, idata=poke.idata) Error in anova.mlm(multmodel, M = ~Afac * Bfac, X = ~Afac + Bfac, idata = poke.idata) : residuals have rank 4 6 which in turn is equivalent to anova(multmodel, idata=poke.idata, X=~Afac+Bfac) Error in anova.mlm(multmodel, idata = poke.idata, X = ~Afac + Bfac) : residuals have rank 4 6 both of which fail for the same reason that Anova() does: Because the within-subject interaction has 6 df and there are just 5 subjects, the residual SSP matrix, say SSPE, is of rank 4. The hypothesis of no interaction has (3 - 1)*(4 - 1) = 6 df, and thus the response-transformation matrix for this hypothesis, say P, has 6 columns. The error SSP matrix for the interaction, t(P) %*% SSPE %*% P, is also therefore of rank 4 6. I believe that under these circumstances, it's possible to do the univariate F-tests but not the multivariate repeated-measures ANOVA. Since Anova() always computes the multivariate tests, however, I don't see a way around the problem without entirely changing how Anova() gets the univariate tests. What's unclear to me is whether the full data set really has just 5 subjects. Regards, John John Fox Senator William McMaster Professor of Social Statistics Department of Sociology McMaster University Hamilton, Ontario, Canada web: socserv.mcmaster.ca/jfox -Original Message- From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On Behalf Of Peter Dalgaard Sent: January-03-10 5:32 AM To: Colleen F. Moore Cc: r-help@r-project.org Subject: Re: [R] Anova in 'car': SSPE apparently deficient rank Colleen F. Moore wrote: I have design with two repeated-measures factor, and no grouping factor. I can analyze the dataset successfully in other software, including my legacy DOS version BMDP, and R's 'aov' function. I would like to use 'Anova' in 'car' in order to obtain the sphericity tests and the H-F corrected p-values. I do not believe the data are truly deficient in rank. I followed the methods for this kind of analysis outlined in Bennett's excellent handouts for his Psychology 710 course http://www.psychology.mcmaster.ca/bennett/psy710/lectures/maxwell_chp12.pdf I am trying to convert my own similar course to R for my students for next fall. I have been successful at analyzing a segment of the data as a 2-way repeated measures design. Here is my code: your.data=read.table(pipe(pbpaste),header=T) your.data partic A1B1 A1B2 A1B3 A1B4 A2B1 A2B2 A2B3 A2B4 A3B1 A3B2 A3B3 A3B4 1 p111231247137 10 2 p2223322562469 3 p3122323261479 4 p411221236238 10 5 p5223323572379 attach(your.data) multmodel=lm(cbind(A1B1, A1B2, A1B3, A1B4, A2B1, A2B2, A2B3, A2B4, A3B1, A3B2, A3B3, A3B4)~1) poke.idata=read.table(pipe(pbpaste),header=T) poke.idata Afac Bfac 1A1 B1 2A1 B2 3A1 B3 4A1 B4 5A2 B1 6A2 B2 7A2 B3 8A2 B4 9A3 B1 10 A3 B2 11 A3 B3 12 A3 B4 attach(poke.idata) pokeAnova =Anova(multmodel,idata=poke.idata,idesign=~Afac*Bfac,type=III) Error in linear.hypothesis.mlm(mod, hyp.matrix, SSPE = SSPE, idata = idata, : The error SSP matrix is apparently of deficient rank = 4 6 Thanks for any help or advice. And thanks for the 'car' package, which is a great asset to my course. I'm just stuck on this one example. Hmm, this does seem to work with regular anova.mlm: anova(multmodel, idata=poke.idata, X=~Afac+Bfac,test=Sph) Analysis of Variance Table Contrasts orthogonal to ~Afac + Bfac Greenhouse-Geisser epsilon: 0.2880 Huynh-Feldt epsilon:0.4871 Df F num Df den DfPr(F) G-G Pr H-F Pr (Intercept) 1 36.67 6 24 6.164e-11 2.5249e-04 3.3530e-06 Residuals4 As far as I recall, the epsilon corrections do not have a formal requirement of a nonsingular SSD of the relevant contrast. Not sure about the accuracy of the F probabilities in such cases, though. -- O__ Peter Dalgaard Øster Farimagsgade 5, Entr.B c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~ - (p.dalga...@biostat.ku.dk) FAX: (+45) 35327907 __ 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
Re: [R] Anova in 'car': SSPE apparently deficient rank
John Fox wrote: Dear Peter and Colleen, I think that Peter realizes this, but what Anova() does in this case is equivalent to the MANOVA anova(multmodel, M = ~ Afac*Bfac, X = ~Afac + Bfac, idata=poke.idata) Error in anova.mlm(multmodel, M = ~Afac * Bfac, X = ~Afac + Bfac, idata = poke.idata) : residuals have rank 4 6 which in turn is equivalent to anova(multmodel, idata=poke.idata, X=~Afac+Bfac) Error in anova.mlm(multmodel, idata = poke.idata, X = ~Afac + Bfac) : residuals have rank 4 6 both of which fail for the same reason that Anova() does: Because the within-subject interaction has 6 df and there are just 5 subjects, the residual SSP matrix, say SSPE, is of rank 4. The hypothesis of no interaction has (3 - 1)*(4 - 1) = 6 df, and thus the response-transformation matrix for this hypothesis, say P, has 6 columns. The error SSP matrix for the interaction, t(P) %*% SSPE %*% P, is also therefore of rank 4 6. I believe that under these circumstances, it's possible to do the univariate F-tests but not the multivariate repeated-measures ANOVA. Since Anova() always computes the multivariate tests, however, I don't see a way around the problem without entirely changing how Anova() gets the univariate tests. Yep. Just let me add that what you call univariate is what I call spherical, i.e. it is based on the assumption that the true error covariance matrix t(P) %*% Sigma %*% P is proportional t(P) %*% P. What's unclear to me is whether the full data set really has just 5 subjects. These things do happen... -- O__ Peter Dalgaard Øster Farimagsgade 5, Entr.B c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~ - (p.dalga...@biostat.ku.dk) FAX: (+45) 35327907 __ 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.
