# Example from https://parsnip.tidymodels.org/articles/Examples.html#linear_reg-models
library(tidymodels)
library(performance)
#> 
#> Attaching package: 'performance'
#> The following objects are masked from 'package:yardstick':
#> 
#>     mae, rmse
tidymodels_prefer()
data(Chicago)

n <- nrow(Chicago)
Chicago <- Chicago %>% select(ridership, Clark_Lake, Quincy_Wells)

Chicago_train <- Chicago[1:(n - 7), ]
Chicago_test <- Chicago[(n - 6):n, ]

linreg_reg_spec <- 
  linear_reg() %>% 
  set_engine("glm")
linreg_reg_spec
#> Linear Regression Model Specification (regression)
#> 
#> Computational engine: glm

set.seed(1)
linreg_reg_fit <- linreg_reg_spec %>% fit(ridership ~ ., data = Chicago_train)
linreg_reg_fit
#> parsnip model object
#> 
#> 
#> Call:  stats::glm(formula = ridership ~ ., family = stats::gaussian, 
#>     data = data)
#> 
#> Coefficients:
#>  (Intercept)    Clark_Lake  Quincy_Wells  
#>       1.6624        0.7738        0.2557  
#> 
#> Degrees of Freedom: 5690 Total (i.e. Null);  5688 Residual
#> Null Deviance:       245000 
#> Residual Deviance: 53530     AIC: 28910

# Define model checking function

check_convergence._glm <- function(x, ...){
  isTRUE(x$fit$converged)
}

# Apply model checking function

linreg_reg_fit %>% 
  check_convergence._glm()
#> [1] TRUE