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 modeling function. This includes the name of the modeling function or any arguments passed to the modeling 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`

.

Glance returns the same number of columns regardless of whether the
model matrix is rank-deficient or not. If so, entries in columns
that no longer have a well-defined value are filled in with an `NA`

of the appropriate type.

## Usage

```
# S3 method for speedglm
glance(x, ...)
```

## Arguments

- x
A

`speedglm`

object returned from`speedglm::speedglm()`

.- ...
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:

## See also

Other speedlm tidiers:
`augment.speedlm()`

,
`glance.speedlm()`

,
`tidy.speedglm()`

,
`tidy.speedlm()`

## Value

A `tibble::tibble()`

with exactly one row and columns:

- AIC
Akaike's Information Criterion for the model.

- BIC
Bayesian Information Criterion for the model.

- deviance
Deviance of the model.

- df.null
Degrees of freedom used by the null model.

- df.residual
Residual degrees of freedom.

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

- nobs
Number of observations used.

- null.deviance
Deviance of the null model.

## Examples

```
# load libraries for models and data
library(speedglm)
# generate data
clotting <- data.frame(
u = c(5, 10, 15, 20, 30, 40, 60, 80, 100),
lot1 = c(118, 58, 42, 35, 27, 25, 21, 19, 18)
)
# fit model
fit <- speedglm(lot1 ~ log(u), data = clotting, family = Gamma(log))
# summarize model fit with tidiers
tidy(fit)
#> # A tibble: 2 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 5.50 0.190 28.9 0.0000000152
#> 2 log(u) -0.602 0.0553 -10.9 0.0000122
glance(fit)
#> # A tibble: 1 × 8
#> null.deviance df.null logLik AIC BIC deviance df.residual nobs
#> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <int> <int>
#> 1 3.51 8 -26.2 58.5 59.1 0.163 7 9
```