Provides an overview of a model and the terms in that model

## Usage

```
overview(model, ...)
# S3 method for class 'gam'
overview(
model,
parametric = TRUE,
random_effects = TRUE,
dispersion = NULL,
frequentist = FALSE,
accuracy = 0.001,
stars = FALSE,
...
)
```

## Arguments

- model
a fitted model object to overview.

- ...
arguments passed to other methods.

- parametric
logical; include the model parametric terms in the overview?

- random_effects
tests of fully penalized smooth terms (those with a zero-dimensional null space, e.g. random effects) are computationally expensive and for large data sets producing these p values can take a very long time. If

`random_effects = FALSE`

, the tests of the expensive terms will be skipped.- dispersion
numeric; a known value for the dispersion parameter. The default

`NULL`

implies that the estimated value or the default value (1 for the Poisson distribution for example) where this is specified is used instead.- frequentist
logical; by default the Bayesian estimated covariance matrix of the parameter estimates is used to calculate p values for parametric terms. If

`frequentist = FALSE`

, the frequentist covariance matrix of the parameter estimates is used.- accuracy
numeric; accuracy with which to report p values, with p values below this value displayed as

`"< accuracy"`

.- stars
logical; should significance stars be added to the output?

## Examples

```
load_mgcv()
df <- data_sim(n = 400, seed = 2)
m <- gam(y ~ x3 + s(x0) + s(x1, bs = "bs") + s(x2, bs = "ts"),
data = df, method = "REML"
)
overview(m)
#>
#> Generalized Additive Model with 4 terms
#>
#> term type k edf statistic p.value
#> <chr> <chr> <dbl> <dbl> <dbl> <chr>
#> 1 x3 parametric NA 1 4.28 0.03926
#> 2 s(x0) TPRS 9 3.02 6.25 < 0.001
#> 3 s(x1) B spline 9 2.81 71.0 < 0.001
#> 4 s(x2) TPRS (shrink) 9 7.91 83.8 < 0.001
```