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I am fitting a linear mixed effect model in R (function lme), and I get a Var-Cov matrix with negative entries (Log-Cholesky). This does not allow me to compute confidence intervals on the standard deviations of the random effects. Shouldn't this matrix theoretically be postive-definite? Does this warn me of something specific?

The model has two fixed effects (factors) with interaction term, and nested random effects on intercept and slope.

[UPDATE]

I have uploaded the data at this link. The explanation of the experiment and the data-set are detailed in this question. To sanity-check what the problem might be, I first simplify the dataset by considering a single level of the factor spd_des:

> spdDes <- 's15'
> dat <- dat[dat$spd_des==spdDes,]

Nevertheless, when I try to fit a model with random slope on mPair, I get negative entries on the var-cov matrix.

> lmer(cc_marg ~ mPair + (mPair|ratID), data = dat, REML=TRUE, na.action=na.omit)

This is true using lme, lmer, as well as stan_lmer. In particular, stan_lmer returns the warning There were 8 divergent transitions after warmup. When I plot the traces with stan_trace I do not see any convergence: all the traces look like random noise.

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  • $\begingroup$ What is the model formula ? $\endgroup$ – Robert Long May 15 at 15:57
  • $\begingroup$ lme(cc_marg ~ mPair*spd_des , random = ~mPair|ratID/spd_des/cycle, data=dat_trf, na.action=na.omit, method = "ML", control=lCtr ) $\endgroup$ – Cristiano May 15 at 16:22
  • $\begingroup$ That's a lot of random effects. Do you really need random slopes over all 3 levels ?The data may not support such a structure. Is there aný special reason for using lme rather than, for example, lmer in the lme4 package or mixed_model in GLMMadaptive ? $\endgroup$ – Robert Long May 15 at 16:29
  • $\begingroup$ I was actually trying to figure out how to have random slope only on ratId/spd_des, and random intercept also on ratID/spd_des/cycle. How do I set this up? As for lme, I thought it was as good of a package as the other ones. Any specific reason to switch to lmer? $\endgroup$ – Cristiano May 15 at 16:38
  • $\begingroup$ lme4 is much more recent (same primary author) with better support to diagnose and identify problems like these and it allows much more flexibility with random effects provided that you dont want to model the residual covariance structure. $\endgroup$ – Robert Long May 15 at 16:44
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Very often this problem occurs in the optimization routine used by the software, and indicates that the the solution is on or very near the boundary of the parameter space, usually in mixed models, that is a variance component of zero (or a correlation between random effects of 1 or -1). It may be the case that the particular variance component actually is (or is very near) zero (in which case it can be omitted from the model), or that the variance component is meaningfully positive, but the optimization routine fails, often because the number of clusters/levels of the relevant factor is small. Sometimes this can be overcome by using a different optimiser. In R, it is often better, when fitting linear mixed models without a residual covariance structure to use one of the newer packages such as lme4, which offers much better support for diagnosing convergence problems. Another option is to use a Bayesian approach, such as the rstanarm package in R which again can be useful for diagnosing convergence issues. Another package that may be helpful is GLMMadaptive which implements Gaussian Adaptive Qundrature rather than the Laplace method.

Lastly, I would say that in this specific case, the random effects structure is quite complex as it is fitting nested random effects for 3 levels, and random slopes over all 3 levels, and their relevant correlations. This may be an over-specified model, particularly if any of the numbers of clusters are small. I would certainly start with a much simpler model in lme4 with just random intercepts, and add random slopes if warranted by theory to the relevant levels one at a time. Uncorrelated random effects can also be specified if necessary. Building a model up this way will often be a good way to identify and overcome these kinds of problem

Demidenko's book "Mixed Models. Theory and Applications in R" discusses these problems in depth in section 2.15.

The answers to this question and this question are also relevant.

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  • $\begingroup$ I followed your suggestion of using lme4 and simplifying the model. Unfortunately I have the same issue. First, I have reduced the dataset to a single level of the factor spd_des, so I could remove it from the model. Then, I fit a model with random intercept only without any issue. However, when I fit the following model lmer(cc_marg ~ mPair + (mPair|ratID), data = dat_trf, REML=TRUE, na.action=na.omit) I still get negative entry in the var-cov matrix. Knowing the underlying question, I do think that random effects can indeed be highly positively correlated. I am not sure how to proceed. $\endgroup$ – Cristiano May 24 at 22:05
  • $\begingroup$ is mpair a factor ? How many levels does it have ? How many ratIDs are there ? How many observations in total and are there any singleton clusters? $\endgroup$ – Robert Long May 25 at 4:57
  • $\begingroup$ mPair is a factor with 6 levels. There are 10 levels of ratID. For each of those levels of ratID I have about 100 (different number across rats, with a minimum of 30) observations for each level of mPair. There are a few combinations of mPair and ratID with no data (not many), but there is no rats with no observations in any mPair. $\endgroup$ – Cristiano May 25 at 15:16
  • $\begingroup$ Seems reasonable. Try plugging in rstanarm with identical syntax then look at trace plots to see if it is converging to reasonable values. $\endgroup$ – Robert Long May 25 at 17:35
  • $\begingroup$ ok so...I fit the simplified model with stan_lmer. I still obtain negative entries in the var-cov matrix. These entries are all close to zero, and are similar to those obtained with lmer. stan_lmer returns the warning There were 8 divergent transitions after warmup. When I plot the traces with stan_trace I do not see any convergence: all the traces look like random noise. Any clue? $\endgroup$ – Cristiano May 29 at 17:49

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