# Error Model Is Singular

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## In Aov Error() Model Is Singular

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## Rails Model Singular

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## Rails Model Singular Or Plural

Join them; it only takes a minute: Sign up Here's how it works: Anybody can ask a question Anybody can answer The best answers are voted up and rise to the top What can cause a “Error() model is singular error” in aov when fitting a repeated measures ANOVA? up vote 1 down vote favorite 1 I have the following singular model in variogram fit data set: | Scenario 1 | Scenario 2 | |Trial 1|Trial 2| Trial 3|Trial 1|Trial 2| Trial 3| ------------------------------------------------------------------- S1 | ... Condition 1 S2 | ... S3 | ------------------------------------------------------------------- S5 | Condition 2 S6 | S7 | Thus the Trials are nested in the Scenarios and all of them are within subject. I am trying to run an ANOVA on this data set. Here is the model without defining that Scenarios (and Trials) are within subject. my_data.aov <- aov(value~Condition*Trial%in%Scenario,data=my_data) #works fine But when I specify that these are within subject: my_data.aov <- aov(value~Condition*Trial%in%Scenario+Error(Player/(Trial%in%Scenario)),data=my_data) I get the following error In aov(value ~ Condition * Trial % in % Scenario + Error(Player/(Trial %in% : Error() model is singular The closest set-up I could find was Split plot in R but there the subjects are nested inside each Trial not in each Condition. EXAMPLE FILE Here is an example file in long format. What about this approach? If I treat each Trial as a sample, then I can collapse across Scenarios by averaging them, so I will ha

here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings and policies of this site About warning message: : error() model is singular Us Learn more about Stack Overflow the company Business Learn more about hiring mixed anova in r developers or posting ads with us Stack Overflow Questions Jobs Documentation Tags Users Badges Ask Question x Dismiss Join the ezanova r Stack Overflow Community Stack Overflow is a community of 4.7 million programmers, just like you, helping each other. Join them; it only takes a minute: Sign up Repeated-measures / within-subjects ANOVA in R http://stats.stackexchange.com/questions/97165/what-can-cause-a-error-model-is-singular-error-in-aov-when-fitting-a-repeate 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 from an experiment using human participants. It has one DV and three IVs. All http://stackoverflow.com/questions/5694664/repeated-measures-within-subjects-anova-in-r 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 distribution, but without more information it is hard to be sure –richiemorrisroe Apr 17 '11 at 16:46 3 looks like your random effects part is far to complex. Si

does that mean Messages sorted by: [ date ] [ thread ] [ subject ] https://stat.ethz.ch/pipermail/r-help//2012-July/317211.html [ author ] On Jul 2, 2012, at 20:02 , zetwal https://www.r-bloggers.com/two-way-anova-with-repeated-measures/ wrote: > On changing the parameters; using only Error(User), I don't get any warning > on singularity. However most tutorials I saw seemed to point to using > Error(User/(whatever i specified before)) > > Are there some rules which indicated when I should use model is only Error(User) and > Error(User/(...)) Not really, but if you don't have a completely balanced User*A*B design, then aov() likely gets in trouble. If I understand your original message correctly, you have 9 observations per subject but 5 levels of the within-subject factors, so that with N subjects you have 9N observations but the error model model is singular tries to fit 25N parameters and this is what triggers the error message. With just the Error(User) term, the model has just N parameters and things are fine, although I suppose you get the A*B effects tested in both strata. > > Thanks > Pascal > > -- > View this message in context: http://r.789695.n4.nabble.com/Error-model-is-singular-what-does-that-mean-tp4635103p4635181.html > Sent from the R help mailing list archive at Nabble.com. > > ______________________________________________ > R-help at 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. -- Peter Dalgaard, Professor, Center for Statistics, Copenhagen Business School Solbjerg Plads 3, 2000 Frederiksberg, Denmark Phone: (+45)38153501 Email: pd.mes at cbs.dk Priv: PDalgd at gmail.com Previous message: [R] Error() model is singular - what does that mean Next message: [R] Error() model is singular - what does that mean Messages sorted by: [ date ] [ thread ] [ subject ] [ author ] More information about the R-help mailing list

by over 573 bloggers. There are many ways to follow us - By e-mail: On Facebook: If you are an R blogger yourself you are invited to add your own R content feed to this site (Non-English R bloggers should add themselves- here) Jobs for R-usersFinance Manager @ Seattle, U.S.Data Scientist – AnalyticsTransportation Market Research Analyst @ Arlington, U.S.Data AnalystData Scientist for Madlan @ Tel Aviv, Israel Popular Searches web scraping heatmap twitter maps time series boxplot animation shiny how to import image file to R hadoop Ggplot2 trading latex finance eclipse excel quantmod sql googlevis PCA knitr rstudio ggplot market research rattle regression coplot map tutorial rcmdr Recent Posts RcppAnnoy 0.0.8 R code to accompany Real-World Machine Learning (Chapter 2) R Course Finder update ggplot2 2.2.0 coming soon! All the R Ladies One Way Analysis of Variance Exercises GoodReads: Machine Learning (Part 3) Danger, Caution H2O steam is very hot!! R+H2O for marketing campaign modeling Watch: Highlights of the Microsoft Data Science Summit A simple workflow for deep learning gcbd 0.2.6 RcppCNPy 0.2.6 Using R to detect fraud at 1 million transactions per second Introducing the eRum 2016 sponsors Other sites SAS blogs Jobs for R-users Two-Way ANOVA with Repeated Measures August 18, 2015By Chris Wetherill (This article was first published on DataScience+, and kindly contributed to R-bloggers) NOTE: This post only contains information on repeated measures ANOVAs, and not how to conduct a comparable analysis using a linear mixed model. For that, be on the lookout for an upcoming post! When I was studying psychology as an undergraduate, one of my biggest frustrations with R was the lack of quality support for repeated measures ANOVAs.They're a pretty common thing to run into in much psychological research, and having to wade through incomplete and often contradictory advice for conducting them was (and still is) a pain, to put it mildly. Thankfully, though, they're not too tricky to set up once you figure out what you're doing. To get started, let's construct a phony data set where we're measuring participant stress on a 100-point scale. Higher numbers mean the participant is more stressed out. For our experimental manipulation, let's say that participants are exposed to a series of several images presented with various background music playi