Satterthwaite approximation for SEs, CIs and p-valuesSource:
R/p_value_satterthwaite.R, and 1 more
An approximate F-test based on the Satterthwaite (1946) approach.
ci_satterthwaite(model, ci = 0.95, ...) dof_satterthwaite(model) p_value_satterthwaite(model, dof = NULL, ...) se_satterthwaite(model)
A statistical model.
Confidence Interval (CI) level. Default to
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. Unlike simpler approximation heuristics
like the "m-l-1" rule (
dof_ml1), the Satterthwaite approximation is
also applicable in more complex multilevel designs. 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.
Satterthwaite FE (1946) An approximate distribution of estimates of variance components. Biometrics Bulletin 2 (6):110–4.
se_satterthwaite() are small helper-functions
to calculate approximated degrees of freedom and standard errors for model
parameters, based on the Satterthwaite (1946) approach.
dof_ml1() approximate degrees of freedom based on
Kenward-Roger's method or the "m-l-1" rule.