Glance accepts a model object and returns a
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
# S3 method for speedlm glance(x, ...)
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
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 for the model.
The log-likelihood of the model. [stats::logLik()] may be a useful reference.
Needs custom info.
R squared statistic, or the percent of variation explained by the model. Also known as the coefficient of determination.
#> # 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- 3glance(mod)#> # A tibble: 1 x 10 #> r.squared adj.r.squared statistic p.value df logLik AIC BIC #> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl> #> 1 0.826 0.814 69.0 9.39e-12 3 -74.4 157. 163. #> # ... with 2 more variables: df.residual <int>, deviance <dbl>augment(mod)#> Error in data.frame(..., check.names = FALSE): arguments imply differing number of rows: 32, 0