Mixed-effects logistic regression
Shown in the app as mixed_logistic. Part of the method reference, under Mixed-effects models.
When to use
Binary outcome with repeated or clustered measurements: odds ratios for predictors plus a per-cluster random intercept.
What you provide
- Outcome: one two-level column.
- Predictors: one or more columns.
- Cluster (subject or site): one column identifying the cluster.
You can also add a random slope: one numeric predictor whose effect is allowed to differ between clusters.
Options
Missing data
- Complete case (listwise deletion)
Multiple comparisons
- Not adjustable for this method. Where it makes post-hoc comparisons, the procedure applies its own correction.
Confidence level
Every interval in the result is reported at the level you choose:
- 90%
- 95%
- 99%
Related methods
Other methods under Mixed-effects models:
Worked examples
Celeus's result on a published or illustrative dataset, next to an independent computation and, where one exists, a published value. The data themselves are not reproduced here.
lme4::VerbAgg
- Source: Published dataset distributed with an R package
- Licence: lme4 (GPL)
- Reference: De Boeck & Wilson (2004); Bates et al. (2015), J. Statistical Software 67(1)
| Quantity | Celeus | Independent computation | Published value | Agrees |
|---|---|---|---|---|
| Estimate | matches refit (not frozen) | (independent refit) | - | Yes |
| p-value | matches refit (not frozen) | (independent refit) | - | Yes |
| Confidence interval | matches refit (not frozen) | (independent refit) | - | Yes |
| Effect size confidence interval | matches refit (not frozen) | (independent refit) | - | Yes |
lme4::cbpp
- Source: Published dataset distributed with an R package
- Licence: lme4 (GPL)
- Reference: Lesnoff et al. (2004), Am. J. Vet. Res. 65; expanded to one row per animal here, the reference an independent data shape (the aggregated binomial fit)
| Quantity | Celeus | Independent computation | Published value | Agrees |
|---|---|---|---|---|
| Estimate | matches refit (not frozen) | (independent refit) | - | Yes |
| p-value | matches refit (not frozen) | (independent refit) | - | Yes |
| Confidence interval | matches refit (not frozen) | (independent refit) | - | Yes |
| Effect size confidence interval | matches refit (not frozen) | (independent refit) | - | Yes |
| Test statistic for each model term | matches refit (not frozen) | (independent refit) | - | Yes |
| First group of each pairwise comparison | matches refit (not frozen) | (independent refit) | - | Yes |
| Second group of each pairwise comparison | matches refit (not frozen) | (independent refit) | - | Yes |
MASS::bacteria
- Source: Published dataset distributed with an R package
- Licence: MASS (GPL)
- Reference: Menard et al. (1992); Venables & Ripley (2002), Modern Applied Statistics with S, Section 10.4; the reference is lme4::glmer called directly (a wiring check)
| Quantity | Celeus | Independent computation | Published value | Agrees |
|---|---|---|---|---|
| Estimate | matches refit (not frozen) | (independent refit) | - | Yes |
| p-value | matches refit (not frozen) | (independent refit) | - | Yes |
References
- Laird, N. M., Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics 38: 963-974. doi:10.2307/2529876 doi:10.2307/2529876
- Bates, D., Machler, M., Bolker, B., Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software 67: 1-48. doi:10.18637/jss.v067.i01 doi:10.18637/jss.v067.i01