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

[Bert Gunter]

I think you should seek out a local statistician with whom to consult if at
all possible, as the details of your research goals and the nature of the
data you have to meet those goals matter and cannot be effectively
discussed in a remote forum like this. That is, to be blunt, you seem to be
risk of producing junk. Just my opinion, which you are of course free to
ignore. Certainly, no response needed, and I will not say anything further.

Cheers,
Bert

On Tue, May 7, 2024 at 9:12 AM Srinidhi Jayakumar via R-help <
r-help using r-project.org> wrote:

> 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
> > FOREST
> > Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
> > thierry.onkelinx using inbo.be
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> >
> ///////////////////////////////////////////////////////////////////////////////////////////
> > 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-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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> >
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