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-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-devel
