See if this paper may help If it helps reducing the model when you have few observations. the (1|ID) may increase the type 1 error. https://journals.sagepub.com/doi/10.1177/25152459231214454 <https://journals.sagepub.com/doi/10.1177/25152459231214454>
Best > On 6 May 2024, at 07:45, Thierry Onkelinx via R-sig-mixed-models > <r-sig-mixed-mod...@r-project.org> wrote: > > Dear Srinidhi, > > You are trying to fit 1 random intercept and 2 random slopes per > individual, while you have at most 3 observations per individual. You > simply don't have enough data to fit the random slopes. Reduce the random > part to (1|ID). > > Best regards, > > Thierry > > ir. Thierry Onkelinx > Statisticus / Statistician > > Vlaamse Overheid / Government of Flanders > INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND > FOREST > Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance > thierry.onkel...@inbo.be > Havenlaan 88 bus 73, 1000 Brussel > *Postadres:* Koning Albert II-laan 15 bus 186, 1210 Brussel > *Poststukken die naar dit adres worden gestuurd, worden ingescand en > digitaal aan de geadresseerde bezorgd. Zo kan de Vlaamse overheid haar > dossiers volledig digitaal behandelen. Poststukken met de vermelding > ‘vertrouwelijk’ worden niet ingescand, maar ongeopend aan de geadresseerde > bezorgd.* > www.inbo.be > > /////////////////////////////////////////////////////////////////////////////////////////// > To call in the statistician after the experiment is done may be no more > than asking him to perform a post-mortem examination: he may be able to say > what the experiment died of. ~ Sir Ronald Aylmer Fisher > The plural of anecdote is not data. ~ Roger Brinner > The combination of some data and an aching desire for an answer does not > ensure that a reasonable answer can be extracted from a given body of data. > ~ John Tukey > /////////////////////////////////////////////////////////////////////////////////////////// > > <https://www.inbo.be> > > > Op ma 6 mei 2024 om 01:59 schreef Srinidhi Jayakumar via R-sig-mixed-models > <r-sig-mixed-mod...@r-project.org>: > >> I am running a multilevel growth curve model to examine predictors of >> social anhedonia (SA) trajectory through ages 12, 15 and 18. SA is a >> continuous numeric variable. The age variable (Index1) has been coded as 0 >> for age 12, 1 for age 15 and 2 for age 18. I am currently using a time >> varying predictor, stress (LSI), which was measured at ages 12, 15 and 18, >> to examine whether trajectory/variation in LSI predicts difference in SA >> trajectory. LSI is a continuous numeric variable and was grand-mean >> centered before using in the models. The data has been converted to long >> format with SA in 1 column, LSI in the other, ID in another, and age in >> another column. I used the code below to run my model using lmer. However, >> I get the following error. Please let me know how I can solve this error. >> Please note that I have 50% missing data in SA at age 12. >> modelLSI_maineff_RE <- lmer(SA ~ Index1* LSI+ (1 + Index1+LSI |ID), data = >> LSIDATA, control = lmerControl(optimizer ="bobyqa"), REML=TRUE) >> summary(modelLSI_maineff_RE) >> Error: number of observations (=1080) <= number of random effects (=1479) >> for term (1 + Index1 + LSI | ID); the random-effects parameters and the >> residual variance (or scale parameter) are probably unidentifiable >> >> I did test the within-person variance for the LSI variable and the >> within-person variance is significant from the Greenhouse-Geisser, >> Hyunh-Feidt tests. >> >> I also tried control = lmerControl(check.nobs.vs.nRE = "ignore") which gave >> me the following output. modelLSI_maineff_RE <- lmer(SA ~ Index1* LSI+ (1 + >> Index1+LSI |ID), data = LSIDATA, control = lmerControl(check.nobs.vs.nRE = >> "ignore", optimizer ="bobyqa", check.conv.singular = .makeCC(action = >> "ignore", tol = 1e-4)), REML=TRUE) >> >> summary(modelLSI_maineff_RE) >> Linear mixed model fit by REML. t-tests use Satterthwaite's method >> ['lmerModLmerTest'] >> Formula: SA ~ Index1 * LSI + (1 + Index1 + LSI | ID) >> Data: LSIDATA >> Control: lmerControl(check.nobs.vs.nRE = "ignore", optimizer = "bobyqa", >> check.conv.singular = .makeCC(action = "ignore", tol = 1e-04)) >> >> REML criterion at convergence: 7299.6 >> >> Scaled residuals: >> Min 1Q Median 3Q Max >> -2.7289 -0.4832 -0.1449 0.3604 4.5715 >> >> Random effects: >> Groups Name Variance Std.Dev. Corr >> ID (Intercept) 30.2919 5.5038 >> Index1 2.4765 1.5737 -0.15 >> LSI 0.1669 0.4085 -0.23 0.70 >> Residual 24.1793 4.9172 >> Number of obs: 1080, groups: ID, 493 >> >> Fixed effects: >> Estimate Std. Error df t value Pr(>|t|) >> (Intercept) 24.68016 0.39722 313.43436 62.133 < 2e-16 *** >> Index1 0.98495 0.23626 362.75018 4.169 3.83e-05 *** >> LSI -0.05197 0.06226 273.85575 -0.835 0.4046 >> Index1:LSI 0.09797 0.04506 426.01185 2.174 0.0302 * >> Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1 >> >> Correlation of Fixed Effects: >> (Intr) Index1 LSI >> Index1 -0.645 >> LSI -0.032 0.057 >> Index1:LSI 0.015 0.037 -0.695 >> >> I am a little vary of the output still as the error states that I have >> equal observations as the number of random effects (i.e., 3 observations >> per ID and 3 random effects). Hence, I am wondering whether I can simplify >> the model as either of the below models and choose the one with the >> best-fit statistics: >> >> modelLSI2 <- lmer(SA ~ Index1* LSI+ (1 |ID)+ (Index1+LSI -1|ID),data = >> LSIDATA, control = lmerControl(optimizer ="bobyqa"), REML=TRUE) *OR* >> >> modelLSI3 <- lmer(SA ~ Index1* LSI+ (1+LSI |ID),data = LSIDATA, control = >> lmerControl(optimizer ="bobyqa"), REML=TRUE) [image: example of dataset] >> <https://i.sstatic.net/JcRKS2C9.png> >> >> [[alternative HTML version deleted]] >> >> _______________________________________________ >> r-sig-mixed-mod...@r-project.org mailing list >> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models >> > > [[alternative HTML version deleted]] > > _______________________________________________ > r-sig-mixed-mod...@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.