In FrancescoBarile/FJLPackage: OurPackage

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(cpp)
#we estimate linear regression model via Gradient Descent using our generated dataset, to see if the function works well
lm_opt_GD = lm_GD_optimizer(Dep.var ~ . -1, mydataset_noconst, tolerance=1e-3, 
                            maxit=1000, stepsize=1e-5, verbose=T)
lm_opt_GD

#we check estimation capability of lm_GD_optimizer via a real data example: describing fuel consumption with Gross horsepower (hp) 
#and weight (wt) with an higher learning parameter and the same tolerance level
lm_opt_GD_mtcars = lm_GD_optimizer(mpg ~ hp + wt, mtcars, tolerance=1e-3, 
                                   maxit=1000, stepsize=1e-2, verbose=T)
lm_opt_GD_mtcars 

#we check estimation capability of lm_GD_optimizer via a real data example: describing fuel consumption with Gross horsepower (hp) and weight (wt), 
#with a lower learning parameter and the same tolerance level
lm_opt_GD_mtcars = lm_GD_optimizer(mpg ~ hp + wt, mtcars, tolerance=1e-3,
                                   maxit=1000, stepsize=1e-6, verbose=T)
lm_opt_GD_mtcars