Estimate or extract degrees of freedom of models parameters.

## Usage

```
degrees_of_freedom(model, ...)
# S3 method for default
degrees_of_freedom(model, method = "analytical", ...)
dof(model, ...)
```

## Arguments

- model
A statistical model.

- ...
Currently not used.

- method
Can be

`"analytical"`

(default, DoFs are estimated based on the model type),`"residual"`

in which case they are directly taken from the model if available (for Bayesian models, the goal (looking for help to make it happen) would be to refit the model as a frequentist one before extracting the DoFs),`"ml1"`

(see`dof_ml1()`

),`"betwithin"`

(see`dof_betwithin()`

),`"satterthwaite"`

(see`dof_satterthwaite()`

),`"kenward"`

(see`dof_kenward()`

) or`"any"`

, which tries to extract DoF by any of those methods, whichever succeeds. See 'Details'.

## Details

Methods for calculating degrees of freedom:

`"analytical"`

for models of class`lmerMod`

, Kenward-Roger approximated degrees of freedoms are calculated, for other models,`n-k`

(number of observations minus number of parameters).`"residual"`

tries to extract residual degrees of freedom, and returns`Inf`

if residual degrees of freedom could not be extracted.`"any"`

first tries to extract residual degrees of freedom, and if these are not available, extracts analytical degrees of freedom.`"nokr"`

same as`"analytical"`

, but does not Kenward-Roger approximation for models of class`lmerMod`

. Instead, always uses`n-k`

to calculate df for any model.`"normal"`

returns`Inf`

.`"wald"`

returns residual df for models with t-statistic, and`Inf`

for all other models.`"kenward"`

calls`dof_kenward()`

.`"satterthwaite"`

calls`dof_satterthwaite()`

.`"ml1"`

calls`dof_ml1()`

.`"betwithin"`

calls`dof_betwithin()`

.

For models with z-statistic, the returned degrees of freedom for model parameters
is `Inf`

(unless `method = "ml1"`

or `method = "betwithin"`

), because there is
only one distribution for the related test statistic.

## Note

In many cases, `degrees_of_freedom()`

returns the same as `df.residuals()`

,
or `n-k`

(number of observations minus number of parameters). However,
`degrees_of_freedom()`

refers to the model's *parameters* degrees of freedom
of the distribution for the related test statistic. Thus, for models with
z-statistic, results from `degrees_of_freedom()`

and `df.residuals()`

differ.
Furthermore, for other approximation methods like `"kenward"`

or
`"satterthwaite"`

, each model parameter can have a different degree of
freedom.

## Examples

```
model <- lm(Sepal.Length ~ Petal.Length * Species, data = iris)
dof(model)
#> [1] 144 144 144 144 144 144
model <- glm(vs ~ mpg * cyl, data = mtcars, family = "binomial")
dof(model)
#> [1] Inf
# \donttest{
if (require("lme4", quietly = TRUE)) {
model <- lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
dof(model)
}
#> [1] 2.53354 144.66692
if (require("rstanarm", quietly = TRUE)) {
model <- stan_glm(
Sepal.Length ~ Petal.Length * Species,
data = iris,
chains = 2,
refresh = 0
)
dof(model)
}
#> This is rstanarm version 2.32.1
#> - See https://mc-stan.org/rstanarm/articles/priors for changes to default priors!
#> - Default priors may change, so it's safest to specify priors, even if equivalent to the defaults.
#> - For execution on a local, multicore CPU with excess RAM we recommend calling
#> options(mc.cores = parallel::detectCores())
#>
#> Attaching package: ‘rstanarm’
#> The following object is masked from ‘package:psych’:
#>
#> logit
#> The following object is masked from ‘package:boot’:
#>
#> logit
#> The following object is masked from ‘package:parameters’:
#>
#> compare_models
#> [1] 143 143 143 143 143 143 143
# }
```