[R] getResponse(model.lme) yields incorrect number of dimensions error
Dear R experts, I would like to get an R^2 - like value for a multilevel regression using lme. I followed an archived suggestion by José Pinheiro to use the squared correlation between fitted and observed values, i.e., (cor(fitted(model.lme), getResponse(model.lme))^2 but getResponse returns the error message Error in val[, level] : incorrect number of dimensions The same happens with residuals(model.lme) and summary(model.lme) but not other generic functions such as predict, coef, or fitted, which (seem to) work fine. I have searched the archives but could not find a solution--I don't really understand what dimensions R is referring to. Any comments or suggestions would be greatly appreciated! Thanks for your time, and happy new year, Ullrich Below is part of the dataframe and the code: > WMUCTrim Subj Cond Acc S R T 1 11 0.667 1 1 1 2 12 0.8095238 1 0 1 4 14 1.000 1 0 0 6 16 0.9523810 0 0 1 7 17 0.8571429 0 1 0 8 18 1.000 0 0 0 20921 0.3809524 1 1 1 21022 0.9047619 1 0 1 21224 1.000 1 0 0 21426 0.8571429 0 0 1 21527 0.667 0 1 0 21628 1.000 0 0 0 > mlr2 <- summary(lme(Acc ~ R + T + S, random = ~1 | Subj)) > mlr2 Linear mixed-effects model fit by REML Data: NULL AIC BIC logLik -1140.414 -1113.778 576.2068 Random effects: Formula: ~1 | Subj (Intercept) Residual StdDev: 0.07069723 0.08233792 Fixed effects: Acc ~ R + T + S Value Std.Error DF t-value p-value (Intercept) 0.9843537 0.008937809 522 110.13367 0. R1 -0.1139456 0.006958824 522 -16.37426 0. T1 -0.1012472 0.006958824 522 -14.54946 0. S1 -0.0137188 0.006958824 522 -1.97143 0.0492 Correlation: (Intr) R1 T1 R1 -0.260 T1 -0.260 0.000 S1 -0.260 0.000 -0.333 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -4.21190127 -0.46318153 0.02715579 0.58591808 2.57708969 Number of Observations: 630 Number of Groups: 105 > class(mlr2) [1] "summary.lme" "lme" > cor(fitted(mlr2), getResponse(mlr2))^2 Error in val[, level] : incorrect number of dimensions Dr Ullrich Ecker | Postdoctoral Research Fellow | Cognitive Science Laboratories | Room 211 Sanders Building | * School of Psychology | M304 | The University of Western Australia | 35 Stirling Highway | Crawley WA 6009 | Australia | ( 08 6488 3266 | Ê 08 6488 1006 | À 04 5822 0072 | @ ullrich.ec...@uwa.edu.au | i www.cogsciwa.com | [[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.
Re: [R] Tukey HSD (or other post hoc tests) following repeated measures ANOVA
Great, thanks a lot, guys! Only thing is I now have two working versions, that yield *slightly* different results. ACCaov <- aov(Acc ~ Cond + Error(Subj/Cond), WMU3C) ACClme <- lme(Acc ~ Cond, random = ~1 | Subj/Cond, WMU3C) # what does '~1 | Subj/Cond' mean? summary(glht(ACClme, linfct=mcp(Cond="Tukey"))) yielding Linear Hypotheses: Estimate Std. Error z value p value 2 - 1 == 0 0.392560 0.027210 14.427 <1e-05 *** 3 - 1 == 0 0.400372 0.027210 14.714 <1e-05 *** 3 - 2 == 0 0.007812 0.025442 0.3070.95 and ACCaov <- aov(Acc ~ Cond + Error(Subj/Cond), WMU3C) ACCaov2 <- aov(terms(Acc ~ Subj + Cond, WMU3C)) # gives same result as 1st aov, but yields aov not aovlist ACCtukey <- TukeyHSD(ACCaov2, "Cond"); ACCtukey yielding $Cond diff lwrupr p adj 2-1 0.3637756 0.29889404 0.42865721 0.000 3-1 0.3715881 0.30670654 0.43646971 0.000 3-2 0.0078125 -0.05329192 0.06891692 0.9504659 I am trying to work my way through this (so I'm one of the good guys, at least I'm trying to understand my stats... ;-)), but any hint to what solution may be more appropriate to my problem would be much appreciated. Cheers, Ullrich At 03:16 PM 30/05/2008, you wrote: >Hi Ullrich, > > >> The model is > >> RT.aov <- aov(RT~Cond + Error(Subj/Cond), WMU3C) > >> I understand that TukeyHSD only works with an aov object, but that > >> RT.aov is an aovlist object. > >You want to use lme() in package nlme, then glht() in the multcomp package. >This will give you multiplicity adjusted p-values and confidence intervals. > >## Example >require(MASS) ## for oats data set >require(nlme) ## for lme() >require(multcomp) ## for multiple comparison stuff > >Aov.mod <- aov(Y ~ N + V + Error(B/V), data = oats) >Lme.mod <- lme(Y ~ N + V, random = ~1 | B/V, data = oats) > >summary(Aov.mod) >anova(Lme.mod) > >summary(Lme.mod) >summary(glht(Lme.mod, linfct=mcp(V="Tukey"))) > >HTH, Mark. > > >Ullrich Ecker wrote: > > > > Hi everyone, > > > > I am fairly new to R, and I am aware that others have had this > > problem before, but I have failed to solve the problem from previous > > replies I found in the archives. > > > > As this is such a standard procedure in psychological science, there > > must be an elegant solution to this...I think. > > > > I would much appreciate a solution that even I could understand... ;-) > > > > > > Now, I want to calculate a post-hoc test following up a within-subjects > > ANOVA. > > > > The dv is reaction time (RT), there is a 3-level Condition factor > > (Cond; within-subjects), a number of subjects (Subj), and the > > dataframe is called WMU3C. > > > > The model is > > > > > RT.aov <- aov(RT~Cond + Error(Subj/Cond), WMU3C) > > > > I understand that TukeyHSD only works with an aov object, but that > > RT.aov is an aovlist object. > > > > > class(RT.aov) > > [1] "aovlist" "listof" > > > > I've tried to work around it using the "maiz" example in the MMC > > documentation of the HH package (a solution previously recommended), > > but I couldn't get it to work: My best shot was to calculate another > > aov avoiding the error term (I don't see how this could be a feasible > > approach, but that's how I understood the MMC example) and a contrast > > vector (contrasting conditions 2 and 3): > > > > I have to admit that I don't quite understand what I'm doing here > > (not that you couldn't tell) > > > > > RT2.aov <- aov(terms(RT~Subj*Cond, WMU3C)) > > > Cond.lmat <- c(0,1,-1) > > > Tukey <- glht.mmc(RT2.aov, focus = "Cond", focus.lmat = Cond.lmat) > > > > yielding > > > > Error in mvt(lower = carg$lower, upper = carg$upper, df = df, corr = > > carg$corr, : > >NA/NaN/Inf in foreign function call (arg 6) > > In addition: Warning message: > > In cov2cor(covm) : diagonal has non-finite entries > > > > > Tukey > >height > > > > > > > > Thank you very much for your help! > > > > Ullrich > > > > > > Dr Ullrich Ecker > > Postdoctoral Research Associate > > Cognitive Science Laboratories > > School of Psychology (Mailbag M304) > > Room 211 Sanders Building > > University of Western Australia > > 35 Stirling Hwy > > Crawley WA 6009 > > Australia > > Office: +61 8 6488 3266 > > Mobile: +61 4 5822 0072 >
Re: [R] Tukey HSD (or other post hoc tests) following repeated measures ANOVA
Thanks, Dieter, but as far as I understand, 'glht' does not support objects of class 'aovlist' either. I mean, I know there is a "TukeyHSD" function out there, but that's the problem: repeated measures ANOVA yields an aovlist object, and TukeyHSD calls for an aov object. And I don't know if confidence intervals are "certainly much better than p-values". What I do know is that most journals of psychology (and medicine, neuroscience, ...for that matter) usually require p-values be reported. Still hope someone can help... Ullrich At 06:31 PM 28/05/2008, you wrote: Ullrich Ecker uwa.edu.au> writes: > I am fairly new to R, and I am aware that others have had this > problem before, but I have failed to solve the problem from previous > replies I found in the archives. > > Now, I want to calculate a post-hoc test following up a within-subjects ANOVA. Probably the best is package multcomp. By default, it gives confidence intervals, which is certainly much better than p-values. Haven't tried if you can get p-values. Dieter library(multcomp) amod <- aov(breaks ~ wool + tension, data = warpbreaks) wht <- glht(amod, linfct = mcp(tension = "Tukey")) __ 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.
[R] Tukey HSD (or other post hoc tests) following repeated measures ANOVA
Hi everyone, I am fairly new to R, and I am aware that others have had this problem before, but I have failed to solve the problem from previous replies I found in the archives. As this is such a standard procedure in psychological science, there must be an elegant solution to this...I think. I would much appreciate a solution that even I could understand... ;-) Now, I want to calculate a post-hoc test following up a within-subjects ANOVA. The dv is reaction time (RT), there is a 3-level Condition factor (Cond; within-subjects), a number of subjects (Subj), and the dataframe is called WMU3C. The model is > RT.aov <- aov(RT~Cond + Error(Subj/Cond), WMU3C) I understand that TukeyHSD only works with an aov object, but that RT.aov is an aovlist object. > class(RT.aov) [1] "aovlist" "listof" I've tried to work around it using the "maiz" example in the MMC documentation of the HH package (a solution previously recommended), but I couldn't get it to work: My best shot was to calculate another aov avoiding the error term (I don't see how this could be a feasible approach, but that's how I understood the MMC example) and a contrast vector (contrasting conditions 2 and 3): I have to admit that I don't quite understand what I'm doing here (not that you couldn't tell) > RT2.aov <- aov(terms(RT~Subj*Cond, WMU3C)) > Cond.lmat <- c(0,1,-1) > Tukey <- glht.mmc(RT2.aov, focus = "Cond", focus.lmat = Cond.lmat) yielding Error in mvt(lower = carg$lower, upper = carg$upper, df = df, corr = carg$corr, : NA/NaN/Inf in foreign function call (arg 6) In addition: Warning message: In cov2cor(covm) : diagonal has non-finite entries > Tukey height Thank you very much for your help! Ullrich Dr Ullrich Ecker Postdoctoral Research Associate Cognitive Science Laboratories School of Psychology (Mailbag M304) Room 211 Sanders Building University of Western Australia 35 Stirling Hwy Crawley WA 6009 Australia Office: +61 8 6488 3266 Mobile: +61 4 5822 0072 Fax: +61 8 6488 1006 E-mail: [EMAIL PROTECTED] Web: www.cogsciwa.com [[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.
