function llike = f2_sem(parm,y,x,W)
% PURPOSE: evaluates log-likelihood -- given ML parmaeters
%  spatial error model using sparse matrix algorithms
% ---------------------------------------------------
%  USAGE:llike = f2_sem(parm,y,X,W)
%  where: parm = vector of maximum likelihood parameters
%                parm(1:k-2,1) = b, parm(k-1,1) = rho, parm(k,1) = sige
%         y    = dependent variable vector (n x 1)
%         X    = explanatory variables matrix (n x k)
%         W    = spatial weight matrix
% ---------------------------------------------------
%  RETURNS: a  scalar equal to minus the log-likelihood
%           function value at the ML parameters
%  --------------------------------------------------
%  SEE ALSO: sar, f2_far, f2_sac, f2_sem
% ---------------------------------------------------

% written by: James P. LeSage 2/98
% University of Toledo
% Department of Economics
% Toledo, OH 43606
% jpl@jpl.econ.utoledo.edu

n = length(y);
k = length(parm);
b = parm(1:k-2,1);
lam = parm(k-1,1);
sige = parm(k,1);
spparms('tight'); 
z = speye(n) - 0.1*sparse(W);
p = colmmd(z);
z = speye(n) - lam*sparse(W);
[l,u] = lu(z(:,p));
detval = sum(log(abs(diag(u))));
eD = y - x*b;
epe = eD'*z'*z*eD;
tmp2 = 1/(2*sige);
llike = (n/2)*log(pi) + (n/2)*log(sige) - detval + tmp2*epe;