Implementing gradient descent for linear regression in R

gradDesc.R

# gradient descent function takes five arguments
gradDesc <- function(df, x, y, alpha = 0.01, max_iter = 100000){
   
   # scale x for faster training
   start_time <- proc.time()
   n <- nrow(df)
   x <- as.vector(scale(df[[x]]))
   y <- df[[y]]
   plot(x, y, pch = 16) 
   
   # initialize random weights
   m <- runif(1,0,1)
   c <- runif(1,0,1)
   yhat <- m * x + c
   mse <- (1/n) * sum((y - yhat) ** 2)
   converged <- F
   iteration <- 0
   
   # update weight using GD algorithm
   cat("=== Implementing gradient descent algorithm ===")
   while(converged == F){
      iteration <- iteration + 1
      m_new <- m - alpha * (1/n) * sum((yhat - y) * x)
      c_new <- c - alpha * (1/n) * sum(yhat - y)
      m <- m_new
      c <- c_new
      yhat <- m * x + c
      mse_new <- (1/n) * sum((y - yhat) ** 2)
      
      # if iteration hits max_iter, program ends
      if(iteration == max_iter){
         converged <- T
         abline(c, m)
         return(cat("\nOptimal intercept:", c,
                    "\nOptimal slope:", m,
                    "\nIteration:", iteration,
                    "\nFinal MSE:", mse_new,
                    "\nTime for training:", 
                    (proc.time() - start_time)[1], "seconds."))
      }
   }
}

# test function x=wt, y=mpg 
gradDesc(mtcars, x = "wt", y = "mpg", alpha = 0.001, max_iter = 100000)