In FrancescoBarile/FJLPackage: OurPackage

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

library(cpp)
#we are going to estimate linear regression model via Steepest Descent in its simple version, 
#constructed in C++
#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 initial 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
SD_Cpp = betahat_SD_Cpp(beta0, X, Y, tolerance, maxit)
SD_Cpp