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

[Srinidhi Jayakumar]

Thank you very  much for your responses!

What if I reduce the model to
 modelLSI3 <- lmer(SA ~ Index1* LSI+ (1+LSI |ID),data = LSIDATA, control =
lmerControl(optimizer ="bobyqa"), REML=TRUE).
This would allow me to see the random effects of LSI and I can drop the
random effect of age (Index1) since I can see that in the unconditional
model [model0 <- lmer(SA ~ Index1+ (1+Index1|ID),data = LSIDATA, control =
lmerControl(optimizer ="bobyqa"), REML=TRUE)]. Would the modelLSI3 also
have a type 1 error?

Thank you,
Srinidhi




On Mon, 6 May 2024, 03:11 TT FF, <trashfaket using gmail.com> wrote:

> 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
>
> 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
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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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