library(tidyverse)

models <- tibble::tribble(
  ~model_name,    ~ formula,
  "length-width", Sepal.Length ~ Petal.Width + Petal.Length,
  "interaction",  Sepal.Length ~ Petal.Width * Petal.Length
)

iris |> 
  nest_by(Species) %>% 
  left_join(models, by = character()) %>% 
  rowwise(Species, model_name) %>% 
  mutate(model = list(lm(formula, data = data))) %>% 
  summarise(broom::glance(model))

# A tibble: 6 × 14
# Groups:   Species, model_name [6]
  Species    model_n…¹ r.squ…² adj.r…³ sigma stati…⁴  p.value    df logLik   AIC
  <fct>      <chr>       <dbl>   <dbl> <dbl>   <dbl>    <dbl> <dbl>  <dbl> <dbl>
1 setosa     length-w…   0.112  0.0739 0.339    2.96 6.18e- 2     2  -15.3  38.7
2 setosa     interact…   0.133  0.0760 0.339    2.34 8.54e- 2     3  -14.7  39.5
3 versicolor length-w…   0.574  0.556  0.344   31.7  1.92e- 9     2  -16.0  40.0
4 versicolor interact…   0.577  0.549  0.347   20.9  1.11e- 8     3  -15.9  41.8
5 virginica  length-w…   0.747  0.736  0.327   69.3  9.50e-15     2  -13.5  34.9
6 virginica  interact…   0.757  0.741  0.323   47.8  3.54e-14     3  -12.4  34.9
# … with 4 more variables: BIC <dbl>, deviance <dbl>, df.residual <int>,
#   nobs <int>, and abbreviated variable names ¹​model_name, ²​r.squared,
#   ³​adj.r.squared, ⁴​statistic
# ℹ Use `colnames()` to see all variable names