Changing the Jacobi Method into Gauss-Seidel method

For my numerical methods class, we are tasked with changing the provided Jacobi function into a Gauss-Seidel function. We have to modify the given code so that it is similar. This is my problem; I can do the Gauss-Seidel method, but I'm not sure how to do it by modifying this code. Can anyone help?

    function [x, er, N] = Jacobi(A,b,x1,maxtol,maxitr)
% Inputs/Outputs:
%  A       = the coefficient matrix
%  b       = right-hand-side column vector in Ax=b
%  x1      = vector of initial estimate of the solution
%  maxtol  = maximum error tolerance
%  maxitr  = maximum number of iterations allowed
% Check if input coefficient matrix is square
[m, n] = size(A);
if m~=n
    error('The input matrix is not square')
end
% Check if the input initial estimate solution vector is column or not
if isrow(x1), x1=x1.'; end   % or use x=x(:);
% Initializations
x = [x1, zeros(n,maxitr)];
k = 0;
er = 1;
while er >= maxtol && k < maxitr
    k = k+1;
    fprintf('\n Iteration = %3i\n', k)
    for i = 1:n
        j = [1:i-1,i+1:n];
        x(i,k+1) = (b(i)-sum(A(i,j)*x(j,k)))/A(i,i);
        fprintf('x%i = %f,\t',i,x(i,k+1))
    end
    er = norm(x(:,k+1)-x(:,k))/norm(x(:,k+1));
    fprintf('\nerror = %e\n',er)
end
N = k;
disp(' ')
disp('x =')
disp(x(:,1:N+1).')
  end

[Geoff Hayes]

jeff - what is the algorithm for Gauss-Seidel? What are the similarities between it and the Jacobi? The function signature would be the same for both algorithms (with a name change only) and the bodies would be similar too: both have an outer while loop to check for convergence, both are trying to find the x, and both have inner for loops. I suspect that the main differences then would be how the x and the er are determined. Since you can do Gauss-Seidel (as stated above), then all you "should" need to do is to replace how x and er are calculated.