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 cross 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, parametric = FALSE, ...)

## Arguments

x |
A `gam` object returned from a call to `mgcv::gam()` . |

parametric |
Logical indicating if parametric or smooth terms should
be tidied. Defaults to `FALSE` , meaning that smooth terms are tidied
by default. |

... |
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

When `parametric = TRUE`

return columns `edf`

and `ref.df`

rather
than `estimate`

and `std.error`

.

To tidy `Gam`

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

,
see `tidy.Gam()`

.

## See also

## Value

A `tibble::tibble()`

with columns:

estimateThe estimated value of the regression term.

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.

std.errorThe standard error of the regression term.

termThe name of the regression term.

## Examples

#> # A tibble: 1 x 5
#> term edf ref.df statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 s(hp) 2.36 3.02 6.34 0.00207

#> # A tibble: 3 x 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 16.7 9.83 1.70 0.101
#> 2 am 4.37 1.56 2.81 0.00918
#> 3 qsec 0.0904 0.525 0.172 0.865

#> # A tibble: 1 x 6
#> df logLik AIC BIC deviance df.residual
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 5.36 -74.4 162. 171. 196. 26.6