An approximate F-test based on the Satterthwaite (1946) approach.
dof_satterthwaite(model)p_value_satterthwaite(model, dof = NULL)
se_satterthwaite(model)
A statistical model.
Degrees of Freedom.
The p-values.
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 statitics. 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<U+2013>4.
dof_satterthwaite() and 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_kenward and dof_ml1 approximate degrees
of freedom bases on Kenward-Roger's method or the "m-l-1" rule.
# NOT RUN {
library(lme4)
model <- lmer(Petal.Length ~ Sepal.Length + (1 | Species), data = iris)
p_value_satterthwaite(model)
# }
# NOT RUN {
# }
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