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...rmediate and output files for project 'replaygain_synthesis_static', configuration 'Debug|Win32' 1>Compiling... 2>Compiling... 2>replaygain_synthesis.c 1>replaygain_analysis.c 1>Creating library... 2>Creating library... 2>Build log was saved at "file://c:\greenhouse\workspace\soundio\tool\flac_1.1.3\main\flac-1.1.3\src\share\replaygain_synthesis\Debug_static\BuildLog.htm" 2>replaygain_synthesis_static - 0 error(s), 0 warning(s) 1>Build log was saved at "file://c:\greenhouse\workspace\soundio\tool\flac_1.1.3\main\flac-1.1.3\src\share\replaygai...

I'm an experimental psychologist and when I run ANOVA analysis in SPSS, I normally ask for a test of non-sphericity (Box's M-test). I also ask for output of the corrections for non-sphericity, such as Greenhouse-Geisser and Huhn-Feldt. These tests and correction factors are commonly used in the journals for experimental and other psychology reports. I have been switching from SPSS to R for over a year now, but I realize now that I don't have the non-sphericity test and correction factors. The backg...

Is there a function in R for doing Greenhouse-Geiser correction in ANOVA models? Is it already available in the aov function? How do we use it? Best, Darren [[alternative HTML version deleted]]

Hi all, I was wondering whether there are any packages that provide for the Greenhouse-Geisser correction, an adjustment used in univariate repeated measures when the sphericity assumption is violated (both numerator and denominator degrees of freedom are multiplied by GG-epsilon, and the significance of the F ratio is evaluated with the new degrees of freedom)? I have seen a fe...

...having 2, 2, and 7 levels. or with an additional fourth between subjects factor D. Everything works fine using aov(val ~ A*B*C + Error(subject/ (A*B*C) ) ) or aov(val ~ (D*A*B*C) + Error(subject/(A*B*C)) + D ) val, A, B, C, D and subject are columns in a data.frame. How can I get the estimated Greenhouse-Geisser and Huynh-Feldt epsilons? I know Peter Dalgaard described it in R-News Vol. 7/2, October 2007. However, unfortunately I am not able to apply that using my data... Furthermore, I am still confused of how SPSS calculates the epsilons since it is mentioned that perhaps there are any errors...

Hi, I've looked in the mailing list archives and found some questions related to Greenhouse-Geisser epsilon correction. Are there any packages providing that? Thanks, Sven -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscri...

...aov(latencies ~ congr*isi + Error(subj/(congr*isi)),data=dats) "congr" has 4 levels, while "isi" has "3" levels. I would like to check for possible violations of the sphericity assumption, and following the example given by Baron and Li(2004), I was able to compute Greenhouse-Geisser epsilon for the effect of "congr" and for the effect of "isi". I calculated the variance/covariance matrix for each of these terms by averaging the "latencies" across the levels of the other factor, but I can't figure out how to calculate the variance/cov...

...o test it in R and how to compute corrected df for the F-test if the assumption is violated. Back when I taught this course using SPSS it was relatively straightforward - we would look at Mauchly's test of sphericity - if it was significant, then we would use one of the corrected F-tests (e.g. Greenhouse-Geisser or Huynh-Feldt) that were spat out automagically by SPSS. I gather from searching the r-help archives, searching google, and searching through various books on R, that the only way of using mauchly.test() in R is on a multivariate model object (e.g. mauchly.test cannot handle an aov() obj...

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...231.0511739 2.4321176 > > Adj Pr > F > Source G - G H - F > > roi <.0001 <.0001 > Error(roi) > > > Greenhouse-Geisser Epsilon 0.5367 > Huynh-Feldt Epsilon 0.6333 > > > Source DF Type III SS Mean Square F Value Pr > F > > ord 1 2.2104107 2.2104107 0.24 0.6276 > Error(ord)...

...nd anything for the topics of random factors in ANOVA, repeated measures in ANOVA, or within subjects designs in R. Could anyone point us to any information concerning - univariate approach to repeated measures anova - multivariate approach to repeated measures anova - mixed designs - Greenhouse-Geisser epsilon correction - Newman-Keuls contrasts - LSR contrasts - Tukey contrasts - Scheffe contrasts Any help or comment appreciated. Since we are not in the mailing list yet, please cc your reply to volker.franz at tuebingen.mpg.de. Thanks, Volker -- ________________________...

Dear all, In a greenhouse experiment we tested performance of 4 different species (B,H,P,R) under 3 different water levels in 10 replications. As response variable e.g. the number of emerging sprouts were measured on three dates. A simple Anova considering every measurement date separately shows a higly significant effect...

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...5.05.10 14:00","26.05.10 19:00","27.05.2010 7:30","27.05.10 14:00","01.06.10 14:00","02.06.10 19:00","23.06.10 12:30"),ordered=TRUE) Pretreatmentf <- as.factor(PAMdata$pretreatment) Provenancef <- as.factor(PAMdata$provenance) Greenhousef <- as.factor(PAMdata$greenhouse) Individualf <- as.factor(PAMdata$individual) PAMval <- (PAMdata$DataPAM) Code<-(PAMdata$code) Thank you for any hint! That Tukey test seems so easy, I just can't find the mistake.... Thank you very much fpr your help and greetings from Tanzania,...

...> mlmfit0 <- lm(data.n ~ 0 + SGROUP) > mlmfit1 <- lm(data.n ~ 1 + SGROUP) > anova(mlmfit1,mlmfit0,test="Spherical",M=~hemi,X=~1) Analysis of Variance Table Model 1: data.n ~ 1 + SGROUP Model 2: data.n ~ 0 + SGROUP Contrasts orthogonal to ~1 Contrasts spanned by ~hemi Greenhouse-Geisser epsilon: 0.6829 Huynh-Feldt epsilon: 0.7127 Res.Df Df Gen.var. F num Df den Df Pr(>F) G-G Pr H-F Pr 1 22 0.54295 2 22 0 0.54295 Inf 0 44 Warning messages: 1: NaNs produced in: pf(q, df1, df2, lower.tail, log.p) 2: NaNs produced in: pf(q, df1, df2, lowe...

...ttA:fB 20.33 2 10.6667 12 11.438 0.001660 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Mauchly Tests for Sphericity Test statistic p-value fB 0.87891 0.7242 fattA:fB 0.87891 0.7242 Greenhouse-Geisser and Huynh-Feldt Corrections for Departure from Sphericity GG eps Pr(>F[GG]) fB 0.89199 0.0001474 *** fattA:fB 0.89199 0.0026452 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 HF eps Pr(>F[...

...2 4 x 2 5 x 2 6 x etc I'm wanting to test for Sphericity (because I've read that you should - is this routine in a repeated measures ANOVA?) and can see that Anova.mlm in the CAR package offers this in addition to the alternative Greenhouse and Feldt tests. I just don't really know how to perform the test - can someone give me some help. Thank you. Paul Dept of Ophthalmology Uni Melbourne, Australia -- View this message in context: http://www.nabble.com/Q-about-how-to-use-Anova.mlm-tp21739443p21739443.html Sent from the R he...

...esign=~zeit) Fehler in L %*% B : nicht passende Argumente (Error in L %*% B : non matching arguments) On the other hand using anova.mlm I get > anova.mlm(lm(mat~1,data),idata=data.frame(zeit=factor(1:3)),X=~1,test="Spherical") Analysis of Variance Table Contrasts orthogonal to ~1 Greenhouse-Geisser epsilon: 0.7464 Huynh-Feldt epsilon: 0.7777 Df F num Df den Df Pr(>F) G-G Pr H-F Pr (Intercept) 1 11.767 2 58 5.1375e-05 3.1183e-04 2.4939e-04 Residuals 29 How can achieve this with...

...mer() is presently assuming no within-group correlation for the data, which I guess is unlikely in my example. Is there a way to compare directly (maybe in terms of log-likelihood?) similar models fitted in lme() and lmer()? 2. Baron & Li suggest a painful (at least for me) procedure to obtain Greenhouse-Geisser or Huyn-Feldt correction for the ANOVA analysis they propose. Is there a package or function which simplifies the procedure? 3. I must admit that I don't understand solution C. I can "hack" it to fit my model, and it seems to work, but I can't seem to grasp the overall c...

...ent, idata=my.data, test="Spherical") Although this yields the result that, after correction, it's not significant - perhaps due to the low DF from this simple example -- Analysis of Variance Table Model 1: response ~ 1 Model 2: response ~ 1 - 1 Contrasts orthogonal to ~Treatment Greenhouse-Geisser epsilon: 0.3565 Huynh-Feldt epsilon: 0.4982 Res.Df Df Gen.var. F num Df den Df Pr(>F) G-G Pr H-F Pr 1 4 0.43167 2 5 1 0.50937 3.7939 4 16 0.02356 0.09620 0.06966 -- But, I still want to get my time and time*treatment interactions - what woul...