Approximation of degrees of freedom based on a "between-within" heuristic.

ci_betwithin(model, ci = 0.95)

dof_betwithin(model)

p_value_betwithin(model, dof = NULL)

se_betwithin(model)

Arguments

model

A mixed model.

ci

Confidence Interval (CI) level. Default to 0.95 (95%).

dof

Degrees of Freedom.

Value

A data frame.

Details

Small Sample Cluster corrected Degrees of Freedom

Inferential statistics (like p-values, confidence intervals and standard errors) may be biased in mixed models when the number of clusters is small (even if the sample size of level-1 units is high). In such cases it is recommended to approximate a more accurate number of degrees of freedom for such inferential statistics (see Li and Redden 2015). The Between-within denominator degrees of freedom approximation is recommended in particular for (generalized) linear mixed models with repeated measurements (longitudinal design). dof_betwithin) implements a heuristic based on the between-within approach. Note that this implementation does not return exactly the same results as shown in Li and Redden 2015, but similar.

Degrees of Freedom for Longitudinal Designs (Repeated Measures)

In particular for repeated measure designs (longitudinal data analysis), the between-within heuristic is likely to be more accurate than simply using the residual or infinite degrees of freedom, because dof_betwithin() returns different degrees of freedom for within-cluster and between-cluster effects.

References

  • Elff, M.; Heisig, J.P.; Schaeffer, M.; Shikano, S. (2019). Multilevel Analysis with Few Clusters: Improving Likelihood-based Methods to Provide Unbiased Estimates and Accurate Inference, British Journal of Political Science.

  • Li, P., Redden, D. T. (2015). Comparing denominator degrees of freedom approximations for the generalized linear mixed model in analyzing binary outcome in small sample cluster-randomized trials. BMC Medical Research Methodology, 15(1), 38. doi: 10.1186/s12874-015-0026-x

See also

dof_betwithin() and se_betwithin() are small helper-functions to calculate approximated degrees of freedom and standard errors of model parameters, based on the "between-within" heuristic.

Examples

# \donttest{
if (require("lme4")) {
  data(sleepstudy)
  model <- lmer(Reaction ~ Days + (1 + Days | Subject), data = sleepstudy)
  dof_betwithin(model)
  p_value_betwithin(model)
}
#>     Parameter            p
#> 1 (Intercept) 9.306054e-80
#> 2        Days 6.290140e-06
# }