Grab other model attributes from `fit()`

[AmeliaMN]

Grab other model attributes from `fit()`

[simonpcouch]

I appreciate you bringing this up, @AmeliaMN!

For now, fit.infer just applies tidy() to the output of each glm() fit. We could maybe open up an interface here for passing an arbitrary model summary function? For the output you mention, folks could pass glance, but I could also imagine folks passing augment, summary, or identity. With glance, that interface could feel something like:

library(tidymodels)

null_dist <- 
   gss %>%
   specify(hours ~ age + college) %>%
   hypothesize(null = "independence") %>%
   generate(reps = 2, type = "permute")

# continue to `tidy() %>% select()` by default
null_dist %>%
   fit()
#> # A tibble: 6 × 3
#> # Groups:   replicate [2]
#>   replicate term          estimate
#>       <int> <chr>            <dbl>
#> 1         1 intercept     41.7    
#> 2         1 age            0.00508
#> 3         1 collegedegree -1.43   
#> 4         2 intercept     40.4    
#> 5         2 age            0.0214 
#> 6         2 collegedegree  0.328

# optional `summary` (or otherwise named) argument
null_dist %>%
   fit(summary = glance)
#> # A tibble: 2 × 13
#> # Groups:   replicate [2]
#>   replicate r.squared adj.r.squ…¹ sigma stati…² p.value    df logLik   AIC   BIC
#>       <int>     <dbl>       <dbl> <dbl>   <dbl>   <dbl> <dbl>  <dbl> <dbl> <dbl>
#> 1         1   0.00121    -0.00281  14.8   0.302   0.740     2 -2057. 4121. 4138.
#> 2         2   0.00275    -0.00126  14.8   0.686   0.504     2 -2056. 4121. 4137.
#> # … with 3 more variables: deviance <dbl>, df.residual <int>, nobs <int>, and
#> #   abbreviated variable names ¹​adj.r.squared, ²​statistic

Actual code for this output:

null_dist %>% 
   nest() %>% 
   rowwise() %>% 
   mutate(mod = list(lm(hours ~ age + college, data = data)), 
          sum = list(glance(mod))) %>% 
   select(-data, -mod) %>% 
   unnest(cols = sum)

Any thoughts on this interface? This feels like a nice way of integrating broom and functions-as-arguments into teaching these pipelines, though I'm not sure if that's Astronaut Stuff Too Early.

One technical hiccup here is that glance() doesn't output $R^2$ for glm() output, though glm() is what's used under the hood in fit.infer() right now.

Also, looping in @mine-cetinkaya-rundel. :)