[R] [R-sig-ME] lmer error: number of observations <= number of random effects

[TT FF]

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-models using 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
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> 
> Op ma 6 mei 2024 om 01:59 schreef Srinidhi Jayakumar via R-sig-mixed-models
> <r-sig-mixed-models using 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>
>> 
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