Glance accepts a model object and returns a `tibble::tibble()`

with exactly one row of model summaries. The summaries are typically
goodness of fit measures, p-values for hypothesis tests on residuals,
or model convergence information.

Glance never returns information from the original call to the modelling
function. This includes the name of the modelling function or any
arguments passed to the modelling function.

Glance does not calculate summary measures. Rather, it farms out these
computations to appropriate methods and gathers the results together.
Sometimes a goodness of fit measure will be undefined. In these cases
the measure will be reported as `NA`

.

# S3 method for gam
glance(x, ...)

## Arguments

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

To glance `Gam`

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

, see
`glance.Gam()`

.

## See also

## Value

A `tibble::tibble()`

with exactly one row and columns:

AICAkaike's Information Criterion for the model.

BICBayesian Information Criterion for the model.

devianceDeviance of the model.

dfDegrees of freedom used by the model.

df.residualResidual degrees of freedom for the model.

logLikThe log-likelihood of the model. [stats::logLik()] may be a useful reference.

## 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