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.

# 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` . Additionally, if you pass
`newdata = my_tibble` to an `augment()` method that does not
accept a `newdata` argument, it will use the default value for
the `data` argument. |

## Details

Tidy `gam`

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

with
`tidy.gam()`

.

## See also

## Value

A `tibble::tibble()`

with columns:

dfDegrees of freedom used by this term in the model.

meansqMean sum of squares. Equal to total sum of squares divided by degrees of freedom.

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

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

sumsqSum of squares explained by this term.

termThe name of the regression term.

## Examples

#> Loading required package: splines

#> Loading required package: foreach

#> Loaded gam 1.20

#> # 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

#> # 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