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.

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

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

Arguments

x

A mable.

...

Arguments for model methods.

Examples

if (requireNamespace("fable", quietly = TRUE)) { library(fable) library(tsibbledata) olympic_running %>% model(lm = TSLM(log(Time) ~ trend())) %>% glance() }
#> New names: #> * glanced -> glanced...1 #> * glanced -> glanced...2 #> * glanced -> glanced...3 #> * glanced -> glanced...4 #> * glanced -> glanced...5 #> * ...
#> # A tibble: 14 x 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 NA NA 4.31e-4 NA NA 2 #> 2 100 women lm NA NA 3.84e-4 NA NA 2 #> 3 200 men lm NA NA 2.47e-4 NA NA 2 #> 4 200 women lm 0.688 0.669 4.77e-4 35.3 2.07e-5 2 #> 5 400 men lm NA NA 5.10e-4 NA NA 2 #> 6 400 women lm 0.311 0.253 4.21e-4 5.41 3.83e-2 2 #> 7 800 men lm NA NA 7.93e-4 NA NA 2 #> 8 800 women lm NA NA 7.42e-4 NA NA 2 #> 9 1500 men lm NA NA 1.12e-3 NA NA 2 #> 10 1500 women lm 0.158 0.0738 4.40e-4 1.88 2.01e-1 2 #> 11 5000 men lm NA NA 3.58e-4 NA NA 2 #> 12 5000 women lm 0.00902 -0.239 1.02e-3 0.0364 8.58e-1 2 #> 13 10000 men lm NA NA 2.63e-4 NA NA 2 #> 14 10000 women lm 0.803 0.770 1.02e-4 24.4 2.60e-3 2 #> # … with 8 more variables: log_lik <dbl>, AIC <dbl>, AICc <dbl>, BIC <dbl>, #> # CV <dbl>, deviance <dbl>, df.residual <int>, rank <int>