Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.

## Usage

```
# S3 method for Gam
tidy(x, ...)
```

## Arguments

- x
A

`Gam`

object returned from a call to`gam::gam()`

.- ...
Additional arguments. Not used. Needed to match generic signature only.

**Cautionary note:**Misspelled arguments will be absorbed in`...`

, where they will be ignored. If the misspelled argument has a default value, the default value will be used. For example, if you pass`conf.lvel = 0.9`

, all computation will proceed using`conf.level = 0.95`

. Two exceptions here are:

## Details

Tidy `gam`

objects created by calls to `mgcv::gam()`

with
`tidy.gam()`

.

## See also

`tidy()`

, `gam::gam()`

, `tidy.anova()`

, `tidy.gam()`

Other gam tidiers:
`glance.Gam()`

## Value

A `tibble::tibble()`

with columns:

- df
Degrees of freedom used by this term in the model.

- meansq
Mean sum of squares. Equal to total sum of squares divided by degrees of freedom.

- p.value
The two-sided p-value associated with the observed statistic.

- statistic
The value of a T-statistic to use in a hypothesis that the regression term is non-zero.

- sumsq
Sum of squares explained by this term.

- term
The name of the regression term.

## Examples

```
# load libraries for models and data
library(gam)
#> Loading required package: splines
#> Loading required package: foreach
#>
#> Attaching package: ‘foreach’
#> The following objects are masked from ‘package:purrr’:
#>
#> accumulate, when
#> Loaded gam 1.22
#>
#> Attaching package: ‘gam’
#> The following objects are masked from ‘package:mgcv’:
#>
#> gam, gam.control, gam.fit, s
# fit model
g <- gam(mpg ~ s(hp, 4) + am + qsec, data = mtcars)
# summarize model fit with tidiers
tidy(g)
#> # A tibble: 4 × 6
#> term df sumsq meansq statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 s(hp, 4) 1 678. 678. 94.4 5.73e-10
#> 2 am 1 113. 113. 15.7 5.52e- 4
#> 3 qsec 1 0.0263 0.0263 0.00366 9.52e- 1
#> 4 Residuals 25.0 180. 7.19 NA NA
glance(g)
#> # A tibble: 1 × 7
#> df logLik AIC BIC deviance df.residual nobs
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 7.00 -76.0 162. 169. 180. 25.0 32
```