Error Model Is Singular Or

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Anybody can answer The best answers are voted up and rise to the top Problem with ANOVA repeated measures: “Error() model is singular” up vote 1 down vote favorite 2 I need an help because I don´t know if the command for the ANOVA analysis I am performing in R is correct. Indeed using the function aov I get the following error: In aov (......) Error() model is singular singular model in variogram fit The structure of my table is the following: subject, stimulus, condition, sex, response Example: subject stimulus condition sex response subject1 gravel EXP1 M 59.8060 subject2 gravel EXP1 M 49.9880 subject3 gravel EXP1 M 73.7420 subject4 gravel EXP1 M 45.5190 subject5 gravel EXP1 M 51.6770 subject6 gravel EXP1 M 42.1760 subject7 gravel EXP1 M 56.1110 subject8 gravel EXP1 M 54.9500 subject9 gravel EXP1 M 62.6920 subject10 gravel EXP1 M 50.7270 subject1 gravel EXP2 M 70.9270 subject2 gravel EXP2 M 61.3200 subject3 gravel EXP2 M 70.2930 subject4 gravel EXP2 M 49.9880 subject5 gravel EXP2 M 69.1670 subject6 gravel EXP2 M 62.2700 subject7 gravel EXP2 M 70.9270 subject8 gravel EXP2 M 63.6770 subject9 gravel EXP2 M 72.4400 subject10 gravel EXP2 M 58.8560 subject11 gravel EXP1 F 46.5750 subject12 gravel EXP1 F 58.1520 subject13 gravel EXP1 F 57.4490 subject14 gravel EXP1 F 59.8770 subject15 gravel EXP1 F 55.5480 subject16 gravel EXP1 F 46.2230 subject17 gravel EXP1 F 63.3260 subject18 gravel EXP1 F 60.6860 subject19 gravel EXP1 F 59.4900 subject20 gravel EXP1 F 52.6630 subject11 gravel EXP2 F 55.7240 subject12 gravel EXP2 F 66.4220 subject13 gravel EXP2 F 65.9300 subject14 gravel EXP2 F 61.8120 subject15 gravel EXP2 F 62.5160 subject16 gravel EXP2 F 65.5780 subject17 gravel EXP2

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Mixed Anova In R

Badges Ask Question x Dismiss Join the Stack Overflow Community Stack Overflow is a community of 4.7 million programmers, just like you, helping each other. Join them; it only takes http://stats.stackexchange.com/questions/11079/problem-with-anova-repeated-measures-error-model-is-singular a minute: Sign up Repeated-measures / within-subjects ANOVA in R up vote 12 down vote favorite 12 I'm attempting to run a repeated-meaures ANOVA using R. I've gone through various examples on various websites, but they never seem to talk about the error that I'm encountering. I assume I'm misunderstanding something important. The ANOVA I'm trying to run is on some data http://stackoverflow.com/questions/5694664/repeated-measures-within-subjects-anova-in-r from an experiment using human participants. It has one DV and three IVs. All of the levels of all of the IVs are run on all participants, making it a three-way repeated-measures / within-subjects ANOVA. The code I'm running in R is as follows: aov.output = aov(DV~ IV1 * IV2 * IV3 + Error(PARTICIPANT_ID / (IV1 * IV2 * IV3)), data=fulldata) When I run this, I get the following warning: Error() model is singular Any ideas what I might be doing wrong? r anova share|improve this question edited Apr 17 '11 at 17:29 Gavin Simpson 104k13209304 asked Apr 17 '11 at 16:15 vize 121119 1 A quick googling of this error (which is often a good tactic) led me to this page: tolstoy.newcastle.edu.au/R/help/04/10/5215.html The relevant part is here: I think that means the correct error model is Error(Subject/T.norm.Class): my guess is that WasSick is a subject-level observation and so each subject only has one level of it. Certainly that is the model which was fitted. - Professor Brian Ripley. /end quote. I suspect that you have specified an incorrect error distribut

this post in threaded view ♦ ♦ | Report Content as Inappropriate ♦ ♦ Error() model is singular - what does that mean Hello I have some test data that looks like that from a within subject experiment. Subject Task-Kind Data-Kind Time-Taken Correct 1 A Data1 5 1 1 A Data1 3 0 1 A Data1 1 1 1 A Data2 8 1 1 A Data2 7 0 1 A Data2 5 0 http://r.789695.n4.nabble.com/Error-model-is-singular-what-does-that-mean-td4635103.html 1 A Data3 2 1 1 A Data3 7 0 1 A Data3 5 0 1 A Data3 6 0 1 B Data1 3 1 1 B Data1 1 1 1 B Data1 3 0 1 http://www.personal.psu.edu/mar36/stat_461/repeated_measures/repeated_measures.html B Data2 9 0 1 B Data2 8 1 1 B Data2 5 0 1 B Data3 2 1 1 B Data3 7 2 1 B Data3 5 3 1 B Data3 6 0 1 model is C Data1 3 1 1 C Data1 1 1 1 C Data1 3 0 1 C Data2 9 0 1 C Data2 8 1 1 C Data2 5 0 1 C Data3 2 1 1 C Data3 7 2 1 C Data3 5 3 1 C Data3 6 0 2 A Data1 5 1 2 A Data1 3 0 2 A Data1 1 1 2 A Data2 8 1 2 A model is singular Data2 7 0 2 A Data2 5 0 2 A Data3 2 1 2 A Data3 7 0 2 A Data3 5 0 2 A Data3 6 0 2 B Data1 3 1 2 B Data1 1 1 2 B Data1 3 0 2 B Data2 9 0 2 B Data2 8 1 2 B Data2 5 0 2 B Data3 2 1 2 B Data3 7 2 2 B Data3 5 3 2 B Data3 6 0 2 C Data1 3 1 2 C Data1 1 1 2 C Data1 3 0 2 C Data2 9 0 2 C Data2 8 1 2 C Data2 5 0 2 C Data3 2 1 2 C Data3 7 2 2 C Data3 5 3 2 C Data3 6 0 . . . some notes: there are 20 subjects there are 5 different kinds of tasks There are 5 different kinds of data and there are several different variations for a certain kind of task and kind of data which is why for Subject = 1 Task-Kind=A and Data-Kind=Data1 we have 3 different results. The measured parameters are time to complete the task and whether it was correct or not (0 implies correct and 1 implies not correct) I am computing the anova as follows: aov.ex = aov(Correct~Task-Kind*Data-Kind+Error(Subject/(Task-Kind*Data-Kind)),data=allDataRaw.xp) since I want to see how the result is affected by the different kinds of data as well as the the kind of task and I get a warning message saying: Error() model is singular I would be very grateful if someone could please tell me what does this mean. Thanks Pascal Jessica Streicher Threaded Open this post in threaded view ♦ ♦ | Report Content as Inappropriate ♦ ♦ Re: Error() model is singular - what does that mean Just looking at it i would try renaming

taste the wine should be random. rm.exp1 <- read.table("CH27TA02.txt", col.names = c("score", "judge", "wine")) rm.exp1 <- within(rm.exp1, c(judgeF <- factor(judge), wineF <- factor(wine))) with(rm.exp1, interaction.plot(x.factor = wineF, trace.factor = judgeF, response = score)) Based on the interaction plot, it does not look like there is an interaction between the judges and the wine. This can be tested with a Tukey test for additivity, which (barley) confirms the lack of an interaction. library(asbio) library(sandwich) with(rm.exp1, tukey.add.test(y = score, A = wineF, B = judgeF)) ## ## Tukey's one df test for additivity ## F = 4.5139 Denom df = 14 p-value = 0.0519 If we ignored the multiple judges, we may not find any differences between the wines. In this example, you do find significant differences, even though the analysis is incorrect. The Tukey HSD would be conservative in this case, failing to find true differences. res1 <- lm(score ~ wineF, data = rm.exp1) anova(res1) ## Analysis of Variance Table ## ## Response: score ## Df Sum Sq Mean Sq F value Pr(>F) ## wineF 3 184 61.3 6.48 0.0031 ** ## Residuals 20 189 9.5 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 TukeyHSD(aov(res1)) ## Tukey multiple comparisons of means ## 95% family-wise confidence level ## ## Fit: aov(formula = res1) ## ## $wineF ## diff lwr upr p adj ## 2-1 2.0000 -2.9720 6.972 0.6783 ## 3-1 6.6667 1.6947 11.639 0.0063 ## 4-1 6.0000 1.0280 10.972 0.0146 ## 3-2 4.6667 -0.3053 9.639 0.0706 ## 4-2 4.0000 -0.9720 8.972 0.1436 ## 4-3 -0.6667 -5.6387 4.305 0.9815 If done correctly, using a block design, we still find a difference among the wines, but there are now more pairwise differences. Which wine or wines is rated the highest? res2 <- lm(score ~ judgeF + wineF, data = rm.exp1) anova(res2) ## Analysis of Variance Table ## ## Response: score ## Df Sum Sq Mean Sq F value Pr(>F) ## judgeF 5 173 34.7 32.5 1.5e-07 *** ## wineF 3 184 61.3 57.5 1.9e-08 *** ## Residuals 15 16 1.1 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 TukeyHSD(aov(res2), "wineF") ## Tukey multiple comparisons of means ## 95% family-wise confidence level ## ## Fit: aov(formula = res2) ## ## $wineF ## diff lw