Uses the models within a mable to produce a one row summary of their fits. This typically contains information about the residual variance, information criterion, and other relevant summary statistics. Each model will be represented with a row of output.
library(fable)
library(tsibbledata)
olympic_running %>%
model(lm = TSLM(log(Time) ~ trend())) %>%
glance()
#> # A tibble: 14 × 17
#> Length Sex .model r_squared adj_r_squared sigma2 statistic p_value df
#> <int> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 100 men lm 0.829 0.823 0.000431 126. 1.78e-11 2
#> 2 100 women lm 0.754 0.741 0.000384 58.1 3.41e- 7 2
#> 3 200 men lm 0.886 0.882 0.000247 195. 2.66e-13 2
#> 4 200 women lm 0.688 0.669 0.000477 35.3 2.07e- 5 2
#> 5 400 men lm 0.843 0.837 0.000510 139. 6.04e-12 2
#> 6 400 women lm 0.311 0.253 0.000421 5.41 3.83e- 2 2
#> 7 800 men lm 0.774 0.765 0.000793 85.8 1.46e- 9 2
#> 8 800 women lm 0.657 0.633 0.000742 26.8 1.40e- 4 2
#> 9 1500 men lm 0.703 0.692 0.00112 61.5 2.55e- 8 2
#> 10 1500 women lm 0.158 0.0738 0.000440 1.88 2.01e- 1 2
#> 11 5000 men lm 0.814 0.806 0.000358 96.6 1.66e- 9 2
#> 12 5000 women lm 0.00902 -0.239 0.00102 0.0364 8.58e- 1 2
#> 13 10000 men lm 0.902 0.897 0.000263 202. 1.45e-12 2
#> 14 10000 women lm 0.803 0.770 0.000102 24.4 2.60e- 3 2
#> # ℹ 8 more variables: log_lik <dbl>, AIC <dbl>, AICc <dbl>, BIC <dbl>,
#> # CV <dbl>, deviance <dbl>, df.residual <int>, rank <int>