An approximate F-test based on the Kenward-Roger (1997) approach.

ci_kenward(model, ci = 0.95)

dof_kenward(model)

p_value_kenward(model, dof = NULL)

se_kenward(model)

Arguments

model

A statistical model.

ci

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

dof

Degrees of Freedom.

Value

A data frame.

Details

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. Unlike simpler approximation heuristics like the "m-l-1" rule (dof_ml1), the Kenward-Roger approximation is also applicable in more complex multilevel designs, e.g. with cross-classified clusters. However, the "m-l-1" heuristic also applies to generalized mixed models, while approaches like Kenward-Roger or Satterthwaite are limited to linear mixed models only.

References

Kenward, M. G., & Roger, J. H. (1997). Small sample inference for fixed effects from restricted maximum likelihood. Biometrics, 983-997.

See also

dof_kenward() and se_kenward() are small helper-functions to calculate approximated degrees of freedom and standard errors for model parameters, based on the Kenward-Roger (1997) approach.

dof_satterthwaite() and dof_ml1() approximate degrees of freedom based on Satterthwaite's method or the "m-l-1" rule.

Examples

# \donttest{
if (require("lme4", quietly = TRUE)) {
  model <- lmer(Petal.Length ~ Sepal.Length + (1 | Species), data = iris)
  p_value_kenward(model)
}
#>      Parameter            p
#> 1  (Intercept) 9.605137e-01
#> 2 Sepal.Length 8.598429e-29
# }