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.

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

x | A |
---|---|

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

Other speedlm tidiers:
`augment.speedlm()`

,
`glance.speedglm()`

,
`tidy.speedglm()`

,
`tidy.speedlm()`

A `tibble::tibble()`

with exactly one row and columns:

Adjusted R squared statistic, which is like the R squared statistic except taking degrees of freedom into account.

Akaike's Information Criterion for the model.

Bayesian Information Criterion for the model.

Deviance of the model.

Degrees of freedom used by the model.

Residual degrees of freedom.

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

Number of observations used.

P-value corresponding to the test statistic.

R squared statistic, or the percent of variation explained by the model. Also known as the coefficient of determination.

F-statistic.

#>#> # A tibble: 3 x 5 #> term estimate std.error statistic p.value #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 (Intercept) 19.7 5.25 3.76 7.65e- 4 #> 2 wt -5.05 0.484 -10.4 2.52e-11 #> 3 qsec 0.929 0.265 3.51 1.50e- 3#> # A tibble: 1 x 11 #> r.squared adj.r.squared statistic p.value df logLik AIC BIC deviance #> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl> #> 1 0.826 0.814 69.0 9.39e-12 3 -74.4 157. 163. 195. #> # … with 2 more variables: df.residual <int>, nobs <int>#> # A tibble: 32 x 6 #> .rownames mpg wt qsec .fitted .resid #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 Mazda RX4 21 2.62 16.5 21.8 -0.815 #> 2 Mazda RX4 Wag 21 2.88 17.0 21.0 -0.0482 #> 3 Datsun 710 22.8 2.32 18.6 25.3 -2.53 #> 4 Hornet 4 Drive 21.4 3.22 19.4 21.6 -0.181 #> 5 Hornet Sportabout 18.7 3.44 17.0 18.2 0.504 #> 6 Valiant 18.1 3.46 20.2 21.1 -2.97 #> 7 Duster 360 14.3 3.57 15.8 16.4 -2.14 #> 8 Merc 240D 24.4 3.19 20 22.2 2.17 #> 9 Merc 230 22.8 3.15 22.9 25.1 -2.32 #> 10 Merc 280 19.2 3.44 18.3 19.4 -0.185 #> # … with 22 more rows