Generalized linear mixed model
Random-intercept model for a clustered binary outcome or a clustered count.
When to use it
Use a GLMM when the outcome is yes-or-no, or a count, and the rows sit inside groups. The binomial family reports an odds ratio. The Poisson family is for counts and drops that column.
The fit is a variational Bayes random-intercept model. A binary outcome with an overall effect, and no random intercept you need to interpret, is GEE.
Assumptions
Binomial needs two outcome values. Poisson needs a non-negative count. The grouping column has at least two clusters. Fixed effects are numeric. This page does not print a convergence warning when the variational fit fails to settle, so read a wild standard error as a reason to rerun on a larger sample.
Running it in Tensr
Options
Prop
Type
Reading the output
The same clinics, with a pass/fail outcome and a moderate hours slope, binomial family.
| Term | Estimate | Std. Err | Odds ratio | p-value |
|---|---|---|---|---|
| const | -1.26627 | 0.189511 | 0.282 | < .001 |
| hours | 0.288825 | 0.0376923 | 1.335 | < .001 |
GLMM (binomial) for passed. GLMM (binomial) for passed. Metrics: Family = binomial; Observations = 120; Groups = 12.
Reporting (APA 7)
GLMM (binomial) for passed. Report the estimate in the table. This procedure is not summarised by one p-value.
Coming from SPSS
The product menu is Multivariate → Mixed Models → Generalized Linear Mixed Model (GLMM). SPSS fits generalized linear mixed models in GENLINMIXED. This Tensr fit is a variational Bayes random intercept.
Related
An overall effect for a clustered yes-or-no outcome, without this random intercept, is GEE. One row per person: logistic regression or Poisson regression.