betahat_GD_R: Estimation of Linear Regression model via Gradient Descent...
In FrancescoBarile/FJLPackage: OurPackage

betahat_GD_R is used to estimates the beta vector of a linear Regression model via an unconstrained optimization method called Gradient Descent Method

1	betahat_GD_R(beta, x, y, tolerance, maxit, stepsize)

`beta`	[numeric] vector containing an initial guess for the beta vector
`x`	[numeric] design matrix
`y`	[numeric] response variable vector
`tolerance`	[numeric] tolerance level, stopping criteria of the algorithm (error<tolerance: stop)
`maxit`	[numeric] maximum number of iterations, used if the stopping criteria is never matched
`stepsize`	[numeric] learning parameter, to update the parameters at each iteration