In FrancescoBarile/FJLPackage: OurPackage

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

library(cpp)

#we estimate linear regression model via Steepest Descent in its simple version, in R
#we use our dataset, without the constant and then transform regressors and response 
#variables in matrices in order to have in the form for being input for the function
XY=data_xy(Dep.var ~ . -1, mydataset_noconst)
Y = as.matrix(XY$Y)
X = as.matrix(XY$X)

#we generate the values of the coefficients from a standard normal
set.seed(1)
beta0 = rnorm(ncol(X))

#we set the tolerance level for stopping: that will be compared to the maximum difference 
#between the observed parameters and the estimated ones
tolerance=1e-3  
#we set the maximum number of iteration, so if the criteria for the stop won't be satisfied,
#the function will stop running anyway
maxit=1000      

#we can put these values as input in order to estimate the parameters for the linear model 
#with the Steepest Descent method, to check how it works
GD_R = betahat_SD_R(beta0, X, Y, tolerance, maxit)

GD_R