# Iterative OLS Regression Using Gauss-Seidel

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I just finished covering a few numerical techniques for solving systems of equations, which can be applied to find best-fit lines through a give set of data points.

The four points are arranged into an inconsistent system of four equations and two unknowns:

The system can be represented in matrix form:

The least-squares solution vector can be found by solving the normal equations

Solving for and yields the following matrix system:

The system can be solved using Gauss-Seidel method.

Below, I run 10 iterations of Gauss-Seidel (visualized in the figure above). The estimated line gets successively closer to the true solution in green. The estimates are shown in blue – each iteration is shown in a darker shade than the next (see highlighted lines).

library(MASS) # package needed for ginv() function A=cbind(c(4,8),c(8,30)) # coefficient matrix b<-t(t(c(12,39))) # b-vector x0<-t(t(c(0,0))) # starting vector iter<-10 # number of Gauss-Seidel iterations to run L<-lower.tri(A)*A # lower triang. A U<-upper.tri(A)*A # upper triang. A D<-diag(diag(A)) # diag of A # plot points xc<-c(0,1,2,5) yc<-c(0,3,3,6) plot(xc,yc, col='red', xlab="X-Values", ylab="Y-Values", bty='n') title(main="OLS Estimates") legend("bottomright", c("Data","Estimates","True Line"), lty=c(0,1,1), pch=c(1,NA,NA), col=c("red","blue","green"), bty='n') # create color palette - lines will get darker with each iter pal<-colorRampPalette(c("#f2f2f2", "Blue")) colors<-pal(iter) # creates color palette of length(iter) # plot true line abline(a=1.0714,b=.8571,col='green') n<-1 while(n<=iter){ print(x0) # Gauss-Seidel formula x1<-(ginv((L+D)))%*%((-U%*%x0)+b) x0<-x1 n=n+1 # plot estimated line abline(a=as.numeric(x0[2,1]), # slope of estimated line b=as.numeric(x0[1,1]), # y-intercept of estimated line col=colors[n]) # pick nth color in palette }

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