From th|erry@onke||nx @end|ng |rom |nbo@be  Mon May  6 08:45:57 2024
From: th|erry@onke||nx @end|ng |rom |nbo@be (Thierry Onkelinx)
Date: Mon, 6 May 2024 08:45:57 +0200
Subject: [Rd] [R-sig-ME] lmer error: number of observations <= number of
 random effects
In-Reply-To: <CAMvZauqY=naWQtSgfp+JtX73vB5+Sm3pO=iv6P6ujNGQHcg5WQ@mail.gmail.com>
References: <CAMvZauqY=naWQtSgfp+JtX73vB5+Sm3pO=iv6P6ujNGQHcg5WQ@mail.gmail.com>
Message-ID: <CAJuCY5ysmodQ=Jt8UGSuimNyz66WS9MAxdxWv4fttrM6O1Tw5A@mail.gmail.com>

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 at 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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>
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> R-sig-mixed-models at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>

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