# Satterthwaite approximation for SEs, CIs and p-values

Source:`R/ci_satterthwaite.R`

, `R/dof_satterthwaite.R`

, `R/p_value_satterthwaite.R`

, and 1 more
`p_value_satterthwaite.Rd`

An approximate F-test based on the Satterthwaite (1946) approach.

## Usage

```
ci_satterthwaite(model, ci = 0.95, ...)
dof_satterthwaite(model)
p_value_satterthwaite(model, dof = NULL, ...)
se_satterthwaite(model)
```

## 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 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.

## References

Satterthwaite FE (1946) An approximate distribution of estimates of variance components. Biometrics Bulletin 2 (6):110–4.

## See also

`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 based on
Kenward-Roger's method or the "m-l-1" rule.