Question
Answer and Explanation
A blank output when using lmer in R, particularly with the summary() function, can be perplexing, but it usually stems from a few common issues. Here are the primary reasons you might encounter a blank or incomplete output:
1. Singular Fit or Convergence Issues:
- The most common reason is that the model failed to converge or resulted in a singular fit. This means the optimization algorithm did not find a stable solution for your mixed-effects model. In such cases, lmer might not produce valid standard errors or other summary statistics, and thus, summary() might return a blank or nearly empty output. The problem often lies with the complexity of the random effects structure in relation to the size of your dataset, leading to an overly complex model that cannot be reliably estimated.
2. Problematic Data:
- Problems within the data itself can cause lmer to fail. This could be due to:
- Perfect Predictors: Having perfect predictors (variables perfectly correlated with the outcome) will lead to a model that cannot be estimated, and therefore, no output.
- Insufficient Variability: If random effects are too similar or show no substantial variance, the algorithm can’t estimate them correctly, leading to a singular fit.
- Highly Skewed Data: The assumption of normality of residuals may be violated, which can impact the reliability of the model fitting procedure.
3. Model Specification Problems:
- An incorrectly specified model formula can also result in errors or blank outputs. Examples include:
- Missing Random Effects: Not including random intercepts or random slopes when needed could cause convergence issues.
- Overly Complex Random Effects: Overly complex or non-identifiable random effect structures (e.g., too many random slopes) can lead to estimation problems.
- Incorrect Syntax: A syntax error in the formula will prevent the model from estimating, thus creating no summary.
4. Underlying Algorithm Limitations:
- While less common, it's possible that the optimization algorithm used by lmer fails under certain conditions, such as an ill-conditioned or pathological likelihood surface.
5. R Environment Issues:
- Sometimes, external factors like a memory limit on your R session can cause instability. However, this often results in error messages, not necessarily blank outputs.
Troubleshooting Steps:
1. Check for Convergence Warnings: First, make sure you inspect the output of the lmer command itself, not just the summary. Look for warning messages related to convergence. These will provide useful clues about the model’s issues.
2. Simplify the Model: Try simplifying your random effects structure. For example, if you have random slopes, try removing those and see if the model can be estimated using only random intercepts.
3. Center or Scale Predictors: Numerical predictors should be centered and/or scaled, as that can often resolve convergence issues.
4. Inspect Data: Make sure your data is appropriately formatted, that all needed values are there, and there are no obvious data problems. Be careful with factors that might have very little representation for some of the groups.
5. Increase Iterations: You can increase the maximum number of iterations in lmer, however, in the case of singular fit, it is unlikely to help.
6. Update R and Packages: Ensuring that R and the necessary packages such as lme4 are up-to-date can sometimes resolve internal bugs that might cause the error.
In summary, a blank output from summary(lmer_model) usually indicates problems with the model estimation, often related to convergence or singularity. Checking convergence warnings, simplifying models, and inspecting your data are the first steps for troubleshooting.