Difference between 3D data fitting functions

Hello,

I am trying to fit a plane to 4 points (x,y,z) and I found two different functions. It should be mentioned, that measurements in x,y,z do not have any errors. The following functions give slighly different results for arbitrary input and I wanted to know, which one is better suited in my case. Thank you!

1) princomp(X) - implemented MATLAB function
2) lsplane(X) - function I found in this forum (see below)

   function [x0, a, d, normd] = lsplane(X)
% ---------------------------------------------------------------------
% LSPLANE.M Least-squares plane (orthogonal distance
% regression).
%
% Version 1.0
% Last amended I M Smith 27 May 2002.
% Created I M Smith 08 Mar 2002
% ---------------------------------------------------------------------
% Input
% X Array [x y z] where x = vector of x-coordinates,
% y = vector of y-coordinates and z = vector of
% z-coordinates.
% Dimension: m x 3.
%
% Output
% x0 Centroid of the data = point on the best-fit plane.
% Dimension: 3 x 1.
%
% a Direction cosines of the normal to the best-fit
% plane.
% Dimension: 3 x 1.
%
% <Optional...
% d Residuals.
% Dimension: m x 1.
%
% normd Norm of residual errors.
% Dimension: 1 x 1.
% ...>
%
% [x0, a <, d, normd >] = lsplane(X)
% ---------------------------------------------------------------------
% check number of data points
   m = size(X, 1);
   if m < 3
     error('At least 3 data points required: ' )
   end
%
% calculate centroid
   x0 = mean(X)';
%
% form matrix A of translated points
   A = [(X(:, 1) - x0(1)) (X(:, 2) - x0(2)) (X(:, 3) - x0(3))];
%
% calculate the SVD of A
   [U, S, V] = svd(A, 0);
%
% find the smallest singular value in S and extract from V the
% corresponding right singular vector
   [s, i] = min(diag(S));
   a = V(:, i);
%
% calculate residual distances, if required
   if nargout > 2
     d = U(:, i)*s;
     normd = norm(d);
   end