function prt_sem(results,vnames,fid)
% PURPOSE: Prints output using spatial error model results structures
%---------------------------------------------------
% USAGE: prt_sem(results,vnames,fid)
% Where: results = a structure returned by a spatial regression 
%        vnames  = an optional vector of variable names
%        fid     = optional file-id for printing results to a file
%                  (defaults to the MATLAB command window)
%--------------------------------------------------- 
%  NOTES: e.g. vnames = strvcat('y','const','x1','x2');
%         e.g. fid = fopen('ols.out','wr');
%  use prt_spat(results,[],fid) to print to a file with no vnames               
% --------------------------------------------------
%  RETURNS: nothing, just prints the spatial regression results
% --------------------------------------------------
% SEE ALSO: prt, plt
%---------------------------------------------------   

% written by:
% James P. LeSage, Dept of Economics
% University of Toledo
% 2801 W. Bancroft St,
% Toledo, OH 43606
% jpl@jpl.econ.utoledo.edu

if ~isstruct(results)
 error('prt_sem requires structure argument');
elseif nargin == 1
 nflag = 0; fid = 1;
elseif nargin == 2
 fid = 1; nflag = 1;
elseif nargin == 3
 nflag = 0;
 [vsize junk] = size(vnames); % user may supply a blank argument
   if vsize > 0
   nflag = 1;          
   end;
else
 error('Wrong # of arguments to prt_sem');
end;


nvar = results.nvar;
nobs = results.nobs;

% handling of vnames
Vname = 'Variable';
 for i=1:nvar
    tmp = ['variable ',num2str(i)];
    Vname = strvcat(Vname,tmp);
 end;
% add spatial rho parameter name
    Vname = strvcat(Vname,'rho');

if (nflag == 1) % the user supplied variable names
[tst_n nsize] = size(vnames);
 if tst_n ~= nvar+1
 fprintf(fid,'Wrong # of variable names in prt_sem -- check vnames argument \n');
 fprintf(fid,'will use generic variable names \n');
 nflag = 0;
 else,
Vname = 'Variable';
 for i=1:nvar
    Vname = strvcat(Vname,vnames(i+1,:));
 end;
% add spatial rho parameter name
    Vname = strvcat(Vname,'rho');
 end; % end of if-else
end; % end of nflag issue



switch results.meth

case {'sem'} % <=================== spatial error model, max lik

nobs = results.nobs;
nvar = results.nvar;

% special handling of vnames
Vname = 'Variable';
 for i=1:nvar
    tmp = ['variable ',num2str(i)];
    Vname = strvcat(Vname,tmp);
 end;
% add spatial rho parameter name
    Vname = strvcat(Vname,'lambda');

if (nflag == 1) % the user supplied variable names
Vname = 'Variable';
[tst_n nsize] = size(vnames);
 if tst_n ~= nvar+1
 fprintf(fid,'Wrong # of variable names in prt_sem -- check vnames argument \n');
 nflag = 0;
 fprintf(fid,'will use generic variable names \n');
 else
 for i=1:nvar
    Vname = strvcat(Vname,vnames(i+1,:));
 end;
% add spatial rho parameter name
    Vname = strvcat(Vname,'lambda');
 end; % end of if-else
end; % end of nflag issue


fprintf(fid,'\n');
fprintf(fid,'Spatial error Model Estimates \n');
if (nflag == 1)
fprintf(fid,'Dependent Variable = %16s \n',vnames(1,:));
end;
fprintf(fid,'R-squared       = %9.4f   \n',results.rsqr);
fprintf(fid,'Rbar-squared    = %9.4f   \n',results.rbar);
fprintf(fid,'sigma^2         = %9.4f   \n',results.sige);
fprintf(fid,'log-likelihood  = %16.8g  \n',results.lik);
fprintf(fid,'Nobs, Nvars     = %6d,%6d \n',results.nobs,results.nvar);
fprintf(fid,'# iterations    = %6d     \n',results.iter);
if strcmp(results.meth,'sem')
fprintf(fid,'min and max rho = %9.4f,%9.4f \n',results.rmin,results.rmax);
end;
% print timing information
fprintf(fid,'total time in secs = %9.4f \n',results.time);
fprintf(fid,'time for optimiz   = %9.4f \n',results.time4);
if results.time1 ~= 0
fprintf(fid,'time for lndet     = %9.4f \n',results.time1);
end;
if results.time2 ~= 0
fprintf(fid,'time for eigs      = %9.4f \n',results.time2);
end;
if results.time3 ~= 0
fprintf(fid,'time for t-stats   = %9.4f \n',results.time3);
end;

if results.lflag == 0
fprintf(fid,'No lndet approximation used \n');
end;
% put in information regarding Pace and Barry approximations
if results.lflag == 1
fprintf(fid,'Pace and Barry, 1999 MC lndet approximation used \n');
fprintf(fid,'order for MC appr  = %6d  \n',results.order);
fprintf(fid,'iter  for MC appr  = %6d  \n',results.miter);
end;
if results.lflag == 2
fprintf(fid,'Pace and Barry, 1998 spline lndet approximation used \n');
end;

fprintf(fid,'***************************************************************\n');

bout = [results.beta
        results.rho];
        
% <=================== end of sem case


case {'sem_g'} % <=================== Gibbs spatial error model


nobs = results.nobs;
nvar = results.nvar;

% handling of vnames
Vname = 'Variable';
 for i=1:nvar
    tmp = ['variable ',num2str(i)];
    Vname = strvcat(Vname,tmp);
 end;
% add spatial rho parameter name
    Vname = strvcat(Vname,'lambda');

if (nflag == 1) % the user supplied variable names
Vname = 'Variable';
[tst_n nsize] = size(vnames);
 if tst_n ~= nvar+1
 fprintf(fid,'Wrong # of variable names in prt_sem -- check vnames argument \n');
 fprintf(fid,'will use generic variable names \n');
 nflag = 0;
 else,
 for i=1:nvar
    Vname = strvcat(Vname,vnames(i+1,:));
 end;
% add spatial rho parameter name
    Vname = strvcat(Vname,'lambda');
 end; % end of if-else
end; % end of nflag issue

  
% find posterior means
    bout = [results.beta
            results.rho];
    sige = results.sige;
    tmp1 = std(results.bdraw);
    tmp2 = std(results.pdraw);
    bstd = [tmp1'
        tmp2];  


if strcmp(results.tflag,'tstat')
 tstat = bout./bstd;
 % find t-stat marginal probabilities
 tout = tdis_prb(tstat,results.nobs);
 results.tstat = bout./bstd; % trick for printing below
else % find plevels
 draws = [results.bdraw results.pdraw];
 for i=1:results.nvar+1;
 if bout(i,1) > 0
 cnt = find(draws(:,i) > 0);
 tout(i,1) = 1 - (length(cnt)/(results.ndraw-results.nomit));
 else
 cnt = find(draws(:,i) < 0);
 tout(i,1) = 1 - (length(cnt)/(results.ndraw-results.nomit));
 end; % end of if - else
 end; % end of for loop
end; 


fprintf(fid,'\n');
fprintf(fid,'Bayesian spatial error model \n');
if results.novi == 1
fprintf(fid,'Homoscedastic version \n');
else
fprintf(fid,'Heteroscedastic version \n');
end;   
if (nflag == 1)
fprintf(fid,'Dependent Variable = %16s \n',vnames(1,:));
end;
fprintf(fid,'R-squared          = %9.4f \n',results.rsqr);
fprintf(fid,'Rbar-squared       = %9.4f \n',results.rbar);
fprintf(fid,'mean of sige draws = %9.4f \n',mean(results.sdraw));
if (results.rdraw == 0 & results.novi == 0)
fprintf(fid,'r-value            = %6d   \n',results.r);
elseif (results.rdraw ~= 0  & results.novi == 0)
fprintf(fid,'mean of rdraws     = %9.4f \n',mean(results.rdraw));
fprintf(fid,'gam(m,k) prior     = %6d,%6d \n',results.m,results.k);
end;    
fprintf(fid,'Nobs, Nvars        = %6d,%6d \n',results.nobs,results.nvar);
fprintf(fid,'ndraws,nomit       = %6d,%6d \n',results.ndraw,results.nomit);
fprintf(fid,'total time in secs = %9.4f   \n',results.time);
if results.time1 ~= 0
fprintf(fid,'time for eigs      = %9.4f \n',results.time1);
end;
if results.time2 ~= 0
fprintf(fid,'time for lndet     = %9.4f \n',results.time2);
end;
if results.time3 ~= 0
fprintf(fid,'time for sampling  = %9.4f \n',results.time3);
end;

if results.lflag == 0
fprintf(fid,'No lndet approximation used \n');
end;
% put in information regarding Pace and Barry approximations
if results.lflag == 1
fprintf(fid,'Pace and Barry, 1999 MC lndet approximation used \n');
fprintf(fid,'order for MC appr  = %6d  \n',results.order);
fprintf(fid,'iter  for MC appr  = %6d  \n',results.iter);
end;
if results.lflag == 2
fprintf(fid,'Pace and Barry, 1998 spline lndet approximation used \n');
end;
fprintf(fid,'metropolis-hastings used for rho \n');  
   

fprintf(fid,'min and max lambda = %9.4f,%9.4f \n',results.rmin,results.rmax);
fprintf(fid,'***************************************************************\n');

% only print priors if non-diffuse
if (results.priorb == 1)
    % non-diffuse prior, so print it
vstring = 'Variable';
bstring = 'Prior Mean';
tstring = 'Std Deviation';

tmp = [results.bmean results.bstd];

cnames = strvcat(bstring,tstring);
rnames = vstring;
for i=1:nvar
rnames = strvcat(rnames,Vname(i+1,:));
end;

pin.fmt = '%16.6f';
pin.fid = fid;
pin.cnames = cnames;
pin.rnames = rnames;

mprint(tmp,pin);
fprintf(fid,'***************************************************************\n');

end; % end of if not a diffuse prior

fprintf(fid,'      Posterior Estimates \n');

 if strcmp(results.tflag,'tstat')
% now print coefficient estimates, t-statistics and probabilities
tout = norm_prb(results.tstat); % find asymptotic z (normal) probabilities
      
tmp = [bout results.tstat tout];  % matrix to be printed
% column labels for printing results
bstring = 'Coefficient'; tstring = 'Asymptot t-stat'; pstring = 'z-probability';
cnames = strvcat(bstring,tstring,pstring);
in.cnames = cnames;
in.rnames = Vname;
in.fmt = '%16.6f';
in.fid = fid;
mprint(tmp,in);
 else % use p-levels for Bayesian results
tmp = [bout bstd tout];  % matrix to be printed
% column labels for printing results
bstring = 'Coefficient'; tstring = 'Std Deviation'; pstring = 'p-level';
cnames = strvcat(bstring,tstring,pstring);
in.cnames = cnames;
in.rnames = Vname;
in.fmt = '%16.6f';
in.fid = fid;
mprint(tmp,in);
end;

return;

% <=================== end of sem_g case


case {'sem_gc'} % <=================== Gibbs spatial error model

nobs = results.nobs;
nvar = results.nvar;

% handling of vnames
Vname = 'Variable';
 for i=1:nvar
    tmp = ['variable ',num2str(i)];
    Vname = strvcat(Vname,tmp);
 end;
% add spatial rho parameter name
    Vname = strvcat(Vname,'lambda');

if (nflag == 1) % the user supplied variable names
Vname = 'Variable';
[tst_n nsize] = size(vnames);
 if tst_n ~= nvar+1
 fprintf(fid,'Wrong # of variable names in prt_sem -- check vnames argument \n');
 fprintf(fid,'will use generic variable names \n');
 nflag = 0;
 else,
 for i=1:nvar
    Vname = strvcat(Vname,vnames(i+1,:));
 end;
% add spatial rho parameter name
    Vname = strvcat(Vname,'lambda');
 end; % end of if-else
end; % end of nflag issue

  
% find posterior means
    bout = [results.beta
            results.rho];
    sige = results.sige;
    tmp1 = std(results.bdraw);
    tmp2 = std(results.pdraw);
    bstd = [tmp1'
        tmp2];  

if strcmp(results.tflag,'tstat')
 tstat = bout./bstd;
 % find t-stat marginal probabilities
 tout = tdis_prb(tstat,results.nobs);
 results.tstat = bout./bstd; % trick for printing below
else % find plevels
 draws = [results.bdraw results.pdraw];
 for i=1:results.nvar+1;
 if bout(i,1) > 0
 cnt = find(draws(:,i) > 0);
 tout(i,1) = 1 - (length(cnt)/(results.ndraw-results.nomit));
 else
 cnt = find(draws(:,i) < 0);
 tout(i,1) = 1 - (length(cnt)/(results.ndraw-results.nomit));
 end; % end of if - else
 end; % end of for loop
end; 


fprintf(fid,'\n');
fprintf(fid,'Bayesian spatial error model \n');
if (nflag == 1)
fprintf(fid,'Dependent Variable = %16s \n',vnames(1,:));
end;
fprintf(fid,'R-squared          = %9.4f \n',results.rsqr);
fprintf(fid,'Rbar-squared       = %9.4f \n',results.rbar);
fprintf(fid,'sigma^2            = %9.4f \n',results.sige);
fprintf(fid,'mean of sige draws = %9.4f \n',mean(results.sdraw));
if results.rdraw == 0
fprintf(fid,'r-value            = %6d   \n',results.r);
else
fprintf(fid,'mean of rdraws     = %9.4f \n',mean(results.rdraw));
fprintf(fid,'gam(m,k) prior     = %6d,%6d \n',results.m,results.k);
end;    
fprintf(fid,'Nobs, Nvars        = %6d,%6d \n',results.nobs,results.nvar);
fprintf(fid,'ndraws,nomit       = %6d,%6d \n',results.ndraw,results.nomit);
fprintf(fid,'total time in secs = %9.4f   \n',results.time);
if results.time1 ~= 0
fprintf(fid,'time for eigs      = %9.4f \n',results.time1);
end;
if results.time2 ~= 0
fprintf(fid,'time for lndet     = %9.4f \n',results.time2);
end;

if results.lflag == 0
fprintf(fid,'No lndet approximation used \n');
end;
% put in information regarding Pace and Barry approximations
if results.lflag == 1
fprintf(fid,'Pace and Barry, 1999 MC lndet approximation used \n');
fprintf(fid,'order for MC appr  = %6d  \n',results.order);
fprintf(fid,'iter  for MC appr  = %6d  \n',results.iter);
end;
if results.lflag == 2
fprintf(fid,'Pace and Barry, 1998 spline lndet approximation used \n');
end;
fprintf(fid,'min and max lambda = %9.4f,%9.4f \n',results.rmin,results.rmax);
fprintf(fid,'***************************************************************\n');

fprintf(fid,'      Posterior Estimates \n');

 if strcmp(results.tflag,'tstat')
% now print coefficient estimates, t-statistics and probabilities
tout = norm_prb(results.tstat); % find asymptotic z (normal) probabilities
      
tmp = [bout results.tstat tout];  % matrix to be printed
% column labels for printing results
bstring = 'Coefficient'; tstring = 'Asymptot t-stat'; pstring = 'z-probability';
cnames = strvcat(bstring,tstring,pstring);
in.cnames = cnames;
in.rnames = Vname;
in.fmt = '%16.6f';
in.fid = fid;
mprint(tmp,in);
 else % use p-levels for Bayesian results
tmp = [bout bstd tout];  % matrix to be printed
% column labels for printing results
bstring = 'Coefficient'; tstring = 'Std Deviation'; pstring = 'p-level';
cnames = strvcat(bstring,tstring,pstring);
in.cnames = cnames;
in.rnames = Vname;
in.fmt = '%16.6f';
in.fid = fid;
mprint(tmp,in);
end;

return;

% <=================== end of sem_g case


case {'semip_g'} % <=================== regression model with individual effects
                %                      that follow a spatial AR model 

nobs = results.nobs;
nvar = results.nvar;

% handling of vnames
Vname = 'Variable';
 for i=1:nvar
    tmp = ['variable ',num2str(i)];
    Vname = strvcat(Vname,tmp);
 end;
% add spatial rho parameter name
    Vname = strvcat(Vname,'rho');

if (nflag == 1) % the user supplied variable names
Vname = 'Variable';
[tst_n nsize] = size(vnames);
 if tst_n ~= nvar+1
 fprintf(fid,'Wrong # of variable names in prt_sem -- check vnames argument \n');
 fprintf(fid,'will use generic variable names \n');
 nflag = 0;
 else,
 for i=1:nvar
    Vname = strvcat(Vname,vnames(i+1,:));
 end;
% add spatial rho parameter name
    Vname = strvcat(Vname,'rho');
 end; % end of if-else
end; % end of nflag issue

  
% find posterior means
tmp1 = mean(results.bdraw);
pout = mean(results.pdraw);
bout = [tmp1'
        pout];

y = results.y;
sige = mean(results.sdraw);
tmp1 = std(results.bdraw);
tmp2 = std(results.pdraw);
bstd = [tmp1'
        tmp2];
if strcmp(results.tflag,'tstat')
 tstat = bout./bstd;
 % find t-stat marginal probabilities
 tout = tdis_prb(tstat,results.nobs);
 results.tstat = bout./bstd; % trick for printing below
else % find plevels
 draws = [results.bdraw results.pdraw];
 for i=1:results.nvar+1;
 if bout(i,1) > 0
 cnt = find(draws(:,i) > 0);
 tout(i,1) = 1 - (length(cnt)/(results.ndraw-results.nomit));
 else
 cnt = find(draws(:,i) < 0);
 tout(i,1) = 1 - (length(cnt)/(results.ndraw-results.nomit));
 end; % end of if - else
 end; % end of for loop
end; 

e = y - results.yhat;
sigu = e'*e;
ym = y - mean(y);
rsqr1 = sigu;
rsqr2 = ym'*ym;
rsqr = 1.0 - rsqr1/rsqr2; % conventional r-squared

fprintf(fid,'\n');
fprintf(fid,'Bayesian probit model with individual spatial effects \n');
if (nflag == 1)
fprintf(fid,'Dependent Variable = %16s \n',vnames(1,:));
end;
%fprintf(fid,'R-squared          = %9.4f \n',rsqr);
fprintf(fid,'sigma^2            = %9.4f \n',sige);
if results.rdraw == 0
fprintf(fid,'r-value            = %6d   \n',results.r);
else
fprintf(fid,'mean of rdraws     = %9.4f \n',mean(results.rdraw));
fprintf(fid,'gam(m,k) prior     = %6d,%6d \n',results.m,results.k);
end;    
fprintf(fid,'Nobs, Nvars        = %6d,%6d \n',results.nobs,results.nvar);
fprintf(fid,'ndraws,nomit       = %6d,%6d \n',results.ndraw,results.nomit);
fprintf(fid,'total time in secs = %9.4f   \n',results.time);
if results.time1 ~= 0
fprintf(fid,'time for eigs      = %9.4f \n',results.time1);
end;
if results.time2 ~= 0
fprintf(fid,'time for lndet     = %9.4f \n',results.time2);
end;
if results.time3 ~= 0
fprintf(fid,'time for sampling  = %9.4f \n',results.time3);
end;

if results.lflag == 0
fprintf(fid,'No lndet approximation used \n');
end;
% put in information regarding Pace and Barry approximations
if results.lflag == 1
fprintf(fid,'Pace and Barry, 1999 MC lndet approximation used \n');
fprintf(fid,'order for MC appr  = %6d  \n',results.order);
fprintf(fid,'iter  for MC appr  = %6d  \n',results.iter);
end;
if results.lflag == 2
fprintf(fid,'Pace and Barry, 1998 spline lndet approximation used \n');
end;

fprintf(fid,'min and max lambda = %9.4f,%9.4f \n',results.rmin,results.rmax);
fprintf(fid,'***************************************************************\n');

vstring = 'Variable';
bstring = 'Prior Mean';
tstring = 'Std Deviation';

tmp = [results.bmean results.bstd];
    

cnames = strvcat(bstring,tstring);
rnames = vstring;
for i=1:nvar
rnames = strvcat(rnames,Vname(i+1,:));
end;

pin.fmt = '%16.6f';
pin.fid = fid;
pin.cnames = cnames;
pin.rnames = rnames;

mprint(tmp,pin);
fprintf(fid,'***************************************************************\n');
fprintf(fid,'      Posterior Estimates \n');

 if strcmp(results.tflag,'tstat')
% now print coefficient estimates, t-statistics and probabilities
tout = norm_prb(results.tstat); % find asymptotic z (normal) probabilities
      
tmp = [bout results.tstat tout];  % matrix to be printed
% column labels for printing results
bstring = 'Coefficient'; tstring = 'Asymptot t-stat'; pstring = 'z-probability';
cnames = strvcat(bstring,tstring,pstring);
in.cnames = cnames;
in.rnames = Vname;
in.fmt = '%16.6f';
in.fid = fid;
mprint(tmp,in);
 else % use p-levels for Bayesian results
tmp = [bout bstd tout];  % matrix to be printed
% column labels for printing results
bstring = 'Coefficient'; tstring = 'Std Deviation'; pstring = 'p-level';
cnames = strvcat(bstring,tstring,pstring);
in.cnames = cnames;
in.rnames = Vname;
in.fmt = '%16.6f';
in.fid = fid;
mprint(tmp,in);
end;

return;

% <=================== end of semip_g case

case {'semit_g'} % <=================== regression model with individual effects
                %                      that follow a spatial AR model 

nobs = results.nobs;
nvar = results.nvar;

% handling of vnames
Vname = 'Variable';
 for i=1:nvar
    tmp = ['variable ',num2str(i)];
    Vname = strvcat(Vname,tmp);
 end;
% add spatial rho parameter name
    Vname = strvcat(Vname,'rho');

if (nflag == 1) % the user supplied variable names
Vname = 'Variable';
[tst_n nsize] = size(vnames);
 if tst_n ~= nvar+1
 fprintf(fid,'Wrong # of variable names in prt_sem -- check vnames argument \n');
 fprintf(fid,'will use generic variable names \n');
 nflag = 0;
 else,
 for i=1:nvar
    Vname = strvcat(Vname,vnames(i+1,:));
 end;
% add spatial rho parameter name
    Vname = strvcat(Vname,'rho');
 end; % end of if-else
end; % end of nflag issue

  
% find posterior means
tmp1 = mean(results.bdraw);
pout = mean(results.pdraw);
bout = [tmp1'
        pout];

y = results.y;
sige = mean(results.sdraw);
tmp1 = std(results.bdraw);
tmp2 = std(results.pdraw);
bstd = [tmp1'
        tmp2];
if strcmp(results.tflag,'tstat')
 tstat = bout./bstd;
 % find t-stat marginal probabilities
 tout = tdis_prb(tstat,results.nobs);
 results.tstat = bout./bstd; % trick for printing below
else % find plevels
 draws = [results.bdraw results.pdraw];
 for i=1:results.nvar+1;
 if bout(i,1) > 0
 cnt = find(draws(:,i) > 0);
 tout(i,1) = 1 - (length(cnt)/(results.ndraw-results.nomit));
 else
 cnt = find(draws(:,i) < 0);
 tout(i,1) = 1 - (length(cnt)/(results.ndraw-results.nomit));
 end; % end of if - else
 end; % end of for loop
end; 

e = y - results.yhat;
sigu = e'*e;
ym = y - mean(y);
rsqr1 = sigu;
rsqr2 = ym'*ym;
rsqr = 1.0 - rsqr1/rsqr2; % conventional r-squared

fprintf(fid,'\n');
fprintf(fid,'Bayesian tobit model with individual spatial effects \n');
if (nflag == 1)
fprintf(fid,'Dependent Variable = %16s \n',vnames(1,:));
end;
%fprintf(fid,'R-squared          = %9.4f \n',rsqr);
fprintf(fid,'sigma^2            = %9.4f \n',sige);
if results.rdraw == 0
fprintf(fid,'r-value            = %6d   \n',results.r);
else
fprintf(fid,'mean of rdraws     = %9.4f \n',mean(results.rdraw));
fprintf(fid,'gam(m,k) prior     = %6d,%6d \n',results.m,results.k);
end;    
fprintf(fid,'Nobs, Nvars        = %6d,%6d \n',results.nobs,results.nvar);
fprintf(fid,'ndraws,nomit       = %6d,%6d \n',results.ndraw,results.nomit);
fprintf(fid,'total time in secs = %9.4f   \n',results.time);
if results.time1 ~= 0
fprintf(fid,'time for eigs      = %9.4f \n',results.time1);
end;
if results.time2 ~= 0
fprintf(fid,'time for lndet     = %9.4f \n',results.time2);
end;
if results.time3 ~= 0
fprintf(fid,'time for sampling  = %9.4f \n',results.time3);
end;

if results.lflag == 0
fprintf(fid,'No lndet approximation used \n');
end;
% put in information regarding Pace and Barry approximations
if results.lflag == 1
fprintf(fid,'Pace and Barry, 1999 MC lndet approximation used \n');
fprintf(fid,'order for MC appr  = %6d  \n',results.order);
fprintf(fid,'iter  for MC appr  = %6d  \n',results.iter);
end;
if results.lflag == 2
fprintf(fid,'Pace and Barry, 1998 spline lndet approximation used \n');
end;

fprintf(fid,'min and max lambda = %9.4f,%9.4f \n',results.rmin,results.rmax);
fprintf(fid,'***************************************************************\n');

vstring = 'Variable';
bstring = 'Prior Mean';
tstring = 'Std Deviation';

tmp = [results.bmean results.bstd];
    

cnames = strvcat(bstring,tstring);
rnames = vstring;
for i=1:nvar
rnames = strvcat(rnames,Vname(i+1,:));
end;

pin.fmt = '%16.6f';
pin.fid = fid;
pin.cnames = cnames;
pin.rnames = rnames;

mprint(tmp,pin);
fprintf(fid,'***************************************************************\n');
fprintf(fid,'      Posterior Estimates \n');

 if strcmp(results.tflag,'tstat')
% now print coefficient estimates, t-statistics and probabilities
tout = norm_prb(results.tstat); % find asymptotic z (normal) probabilities
      
tmp = [bout results.tstat tout];  % matrix to be printed
% column labels for printing results
bstring = 'Coefficient'; tstring = 'Asymptot t-stat'; pstring = 'z-probability';
cnames = strvcat(bstring,tstring,pstring);
in.cnames = cnames;
in.rnames = Vname;
in.fmt = '%16.6f';
in.fid = fid;
mprint(tmp,in);
 else % use p-levels for Bayesian results
tmp = [bout bstd tout];  % matrix to be printed
% column labels for printing results
bstring = 'Coefficient'; tstring = 'Std Deviation'; pstring = 'p-level';
cnames = strvcat(bstring,tstring,pstring);
in.cnames = cnames;
in.rnames = Vname;
in.fmt = '%16.6f';
in.fid = fid;
mprint(tmp,in);
end;

return;

% <=================== end of semit_g case


case {'semt_g','semt_gc'} % <=================== Gibbs spatial error tobit model

nobs = results.nobs;
nvar = results.nvar;

% handling of vnames
Vname = 'Variable';
 for i=1:nvar
    tmp = ['variable ',num2str(i)];
    Vname = strvcat(Vname,tmp);
 end;
% add spatial rho parameter name
    Vname = strvcat(Vname,'lambda');

if (nflag == 1) % the user supplied variable names
Vname = 'Variable';
[tst_n nsize] = size(vnames);
 if tst_n ~= nvar+1
 fprintf(fid,'Wrong # of variable names in prt_sem -- check vnames argument \n');
 fprintf(fid,'will use generic variable names \n');
 nflag = 0;
 else,
 for i=1:nvar
    Vname = strvcat(Vname,vnames(i+1,:));
 end;
% add spatial rho parameter name
    Vname = strvcat(Vname,'lambda');
 end; % end of if-else
end; % end of nflag issue

% find posterior means
    tmp1 = mean(results.bdraw);
    pout = mean(results.pdraw);
    bout = [tmp1'
        pout];
    sige = mean(results.sdraw);
    tmp1 = std(results.bdraw);
    tmp2 = std(results.pdraw);
    bstd = [tmp1'
        tmp2];  


if strcmp(results.tflag,'tstat')
 tstat = bout./bstd;
 % find t-stat marginal probabilities
 tout = tdis_prb(tstat,results.nobs);
 results.tstat = bout./bstd; % trick for printing below
else % find plevels
 draws = [results.bdraw results.pdraw];
 for i=1:results.nvar+1;
 if bout(i,1) > 0
 cnt = find(draws(:,i) > 0);
 tout(i,1) = 1 - (length(cnt)/(results.ndraw-results.nomit));
 else
 cnt = find(draws(:,i) < 0);
 tout(i,1) = 1 - (length(cnt)/(results.ndraw-results.nomit));
 end; % end of if - else
 end; % end of for loop
end; 

y = results.y;
yhat = results.yhat;
e = y - yhat;
sigu = e'*e;
ym = y - mean(y);
rsqr1 = sigu;
rsqr2 = ym'*ym;
rsqr = 1.0 - rsqr1/rsqr2; % conventional r-squared

fprintf(fid,'\n');
fprintf(fid,'Bayesian spatial error Tobit model \n');
if (nflag == 1)
fprintf(fid,'Dependent Variable = %16s \n',vnames(1,:));
end;
fprintf(fid,'R-squared          = %9.4f \n',rsqr);
fprintf(fid,'sigma^2            = %9.4f \n',sige);
if results.rdraw == 0
fprintf(fid,'r-value            = %6d   \n',results.r);
else
fprintf(fid,'mean of rdraws     = %9.4f \n',mean(results.rdraw));
fprintf(fid,'gam(m,k) prior     = %6d,%6d \n',results.m,results.k);
end;    
fprintf(fid,'Nobs, Nvars        = %6d,%6d \n',results.nobs,results.nvar);
fprintf(fid,'# censored values  = %6d \n',results.nobsc);
fprintf(fid,'ndraws,nomit       = %6d,%6d \n',results.ndraw,results.nomit);
fprintf(fid,'time in secs       = %9.4f   \n',results.time);
fprintf(fid,'min and max lambda = %9.4f,%9.4f \n',results.rmin,results.rmax);
fprintf(fid,'***************************************************************\n');

vstring = 'Variable';
bstring = 'Prior Mean';
tstring = 'Std Deviation';

tmp = [results.bmean results.bstd];

cnames = strvcat(bstring,tstring);
rnames = vstring;
for i=1:nvar
rnames = strvcat(rnames,Vname(i+1,:));
end;
pin.fmt = '%16.6f';
pin.fid = fid;
pin.cnames = cnames;
pin.rnames = rnames;

mprint(tmp,pin);
fprintf(fid,'***************************************************************\n');
fprintf(fid,'      Posterior Estimates \n');

 if strcmp(results.tflag,'tstat')
% now print coefficient estimates, t-statistics and probabilities
tout = norm_prb(results.tstat); % find asymptotic z (normal) probabilities
      
tmp = [bout results.tstat tout];  % matrix to be printed
% column labels for printing results
bstring = 'Coefficient'; tstring = 'Asymptot t-stat'; pstring = 'z-probability';
cnames = strvcat(bstring,tstring,pstring);
in.cnames = cnames;
in.rnames = Vname;
in.fmt = '%16.6f';
in.fid = fid;
mprint(tmp,in);
 else % use p-levels for Bayesian results
tmp = [bout bstd tout];  % matrix to be printed
% column labels for printing results
bstring = 'Coefficient'; tstring = 'Std Deviation'; pstring = 'p-level';
cnames = strvcat(bstring,tstring,pstring);
in.cnames = cnames;
in.rnames = Vname;
in.fmt = '%16.6f';
in.fid = fid;
mprint(tmp,in);
 end;

return;

% <=================== end of semt_g case


case {'semp_g','semp_gc'} % <=================== Gibbs spatial error Probit model

nobs = results.nobs;
nvar = results.nvar;

% handling of vnames
Vname = 'Variable';
 for i=1:nvar
    tmp = ['variable ',num2str(i)];
    Vname = strvcat(Vname,tmp);
 end;
% add spatial rho parameter name
    Vname = strvcat(Vname,'lambda');

if (nflag == 1) % the user supplied variable names
Vname = 'Variable';
[tst_n nsize] = size(vnames);
 if tst_n ~= nvar+1
 fprintf(fid,'Wrong # of variable names in prt_sem -- check vnames argument \n');
 fprintf(fid,'will use generic variable names \n');
 nflag = 0;
 else,
 for i=1:nvar
    Vname = strvcat(Vname,vnames(i+1,:));
 end;
% add spatial rho parameter name
    Vname = strvcat(Vname,'lambda');
 end; % end of if-else
end; % end of nflag issue

% find posterior means
tmp1 = mean(results.bdraw);
pout = mean(results.pdraw);
bout = [tmp1'
        pout];
y = results.y;
sige = mean(results.sdraw);
tmp1 = std(results.bdraw);
tmp2 = std(results.pdraw);
bstd = [tmp1'
        tmp2];

if strcmp(results.tflag,'tstat')
 tstat = bout./bstd;
 % find t-stat marginal probabilities
 tout = tdis_prb(tstat,results.nobs);
 results.tstat = bout./bstd; % trick for printing below
else % find plevels
 draws = [results.bdraw results.pdraw];
 for i=1:results.nvar+1;
 if bout(i,1) > 0
 cnt = find(draws(:,i) > 0);
 tout(i,1) = 1 - (length(cnt)/(results.ndraw-results.nomit));
 else
 cnt = find(draws(:,i) < 0);
 tout(i,1) = 1 - (length(cnt)/(results.ndraw-results.nomit));
 end; % end of if - else
 end; % end of for loop
end; 


fprintf(fid,'\n');
if results.novi == 1
fprintf(fid,'Bayesian homoscedastic spatial error Probit model \n');
else
fprintf(fid,'Bayesian heteroscedastic spatial error Probit model \n');
end;
if (nflag == 1)
fprintf(fid,'Dependent Variable = %16s \n',vnames(1,:));
end;
fprintf(fid,'pseudo R-squared   = %9.4f \n',results.rsqr);
fprintf(fid,'sigma^2            = %9.4f \n',sige);
if results.rdraw == 0
fprintf(fid,'r-value            = %6d   \n',results.r);
else
fprintf(fid,'mean of rdraws     = %9.4f \n',mean(results.rdraw));
fprintf(fid,'gam(m,k) prior     = %6d,%6d \n',results.m,results.k);
end;    
fprintf(fid,'Nobs, Nvars        = %6d,%6d \n',results.nobs,results.nvar);
fprintf(fid,'# 0, 1 y-values    = %6d,%6d \n',results.zip,nobs-results.zip);
fprintf(fid,'ndraws,nomit       = %6d,%6d \n',results.ndraw,results.nomit);
fprintf(fid,'min and max lambda = %9.4f,%9.4f \n',results.rmin,results.rmax);
% print timing information
fprintf(fid,'total time in secs = %9.4f \n',results.time);
if results.time1 ~= 0
fprintf(fid,'time for eigs      = %9.4f \n',results.time1);
end;
if results.time2 ~= 0
fprintf(fid,'time for lndet     = %9.4f \n',results.time2);
end;
if results.time3 ~= 0
fprintf(fid,'time for sampling  = %9.4f \n',results.time3);
end;

if results.lflag == 0
fprintf(fid,'No lndet approximation used \n');
end;
% put in information regarding Pace and Barry approximations
if results.lflag == 1
fprintf(fid,'Pace and Barry, 1999 MC lndet approximation used \n');
fprintf(fid,'order for MC appr  = %6d  \n',results.order);
fprintf(fid,'iter  for MC appr  = %6d  \n',results.iter);
end;
if results.lflag == 2
fprintf(fid,'Pace and Barry, 1998 spline lndet approximation used \n');
end;

fprintf(fid,'***************************************************************\n');

vstring = 'Variable';
bstring = 'Prior Mean';
tstring = 'Std Deviation';

tmp = [results.bmean results.bstd];
   
cnames = strvcat(bstring,tstring);
rnames = vstring;
for i=1:nvar
rnames = strvcat(rnames,Vname(i+1,:));
end;

pin.fmt = '%16.6f';
pin.fid = fid;
pin.cnames = cnames;
pin.rnames = rnames;
mprint(tmp,pin);
fprintf(fid,'***************************************************************\n');
fprintf(fid,'      Posterior Estimates \n');

 if strcmp(results.tflag,'tstat')
% now print coefficient estimates, t-statistics and probabilities
tout = norm_prb(results.tstat); % find asymptotic z (normal) probabilities
      
tmp = [bout results.tstat tout];  % matrix to be printed
% column labels for printing results
bstring = 'Coefficient'; tstring = 'Asymptot t-stat'; pstring = 'z-probability';
cnames = strvcat(bstring,tstring,pstring);
in.cnames = cnames;
in.rnames = Vname;
in.fmt = '%16.6f';
in.fid = fid;
mprint(tmp,in);
 else % use p-levels for Bayesian results
tmp = [bout bstd tout];  % matrix to be printed
% column labels for printing results
bstring = 'Coefficient'; tstring = 'Std Deviation'; pstring = 'p-level';
cnames = strvcat(bstring,tstring,pstring);
in.cnames = cnames;
in.rnames = Vname;
in.fmt = '%16.6f';
in.fid = fid;
mprint(tmp,in);
end;

return;

% <=================== end of semp_g case

case {'sar'} % <=================== spatial autoregressive model

nobs = results.nobs;
nvar = results.nvar;

fprintf(fid,'\n');
fprintf(fid,'Spatial autoregressive Model Estimates \n');
if (nflag == 1)
fprintf(fid,'Dependent Variable = %16s \n',vnames(1,:));
end;
fprintf(fid,'R-squared          = %9.4f \n',results.rsqr);
fprintf(fid,'Rbar-squared       = %9.4f \n',results.rbar);
fprintf(fid,'sigma^2            = %9.4f \n',results.sige);
fprintf(fid,'Nobs, Nvars        = %6d,%6d \n',results.nobs,results.nvar);
fprintf(fid,'log-likelihood     = %16.8g \n',results.lik);
fprintf(fid,'# of iterations    = %6d   \n',results.iter);
fprintf(fid,'min and max rho    = %9.4f,%9.4f \n',results.rmin,results.rmax);
% print timing information
fprintf(fid,'total time in secs = %9.4f \n',results.time);
if results.time1 ~= 0
fprintf(fid,'time for lndet     = %9.4f \n',results.time1);
end;
if results.time2 ~= 0
fprintf(fid,'time for eigs      = %9.4f \n',results.time2);
end;
if results.time3 ~= 0
fprintf(fid,'time for t-stats   = %9.4f \n',results.time3);
end;

if results.lflag == 0
fprintf(fid,'No lndet approximation used \n');
end;
% put in information regarding Pace and Barry approximations
if results.lflag == 1
fprintf(fid,'Pace and Barry, 1999 MC lndet approximation used \n');
fprintf(fid,'order for MC appr  = %6d  \n',results.order);
fprintf(fid,'iter  for MC appr  = %6d  \n',results.liter);
end;
if results.lflag == 2
fprintf(fid,'Pace and Barry, 1998 spline lndet approximation used \n');
end;

fprintf(fid,'***************************************************************\n');

bout = [results.beta
        results.rho];
        
% <=================== end of sar case

case {'sar_g','sar_gc'} % <=================== Gibbs spatial autoregressive model

nobs = results.nobs;
nvar = results.nvar;

% find posterior means
    tmp1 = mean(results.bdraw);
    pout = mean(results.pdraw);
    bout = [tmp1'
        pout];
    sige = mean(results.sdraw);
    tmp1 = std(results.bdraw);
    tmp2 = std(results.pdraw);
    bstd = [tmp1'
        tmp2];  

if strcmp(results.tflag,'tstat')
 tstat = bout./bstd;
 % find t-stat marginal probabilities
 tout = tdis_prb(tstat,results.nobs);
 results.tstat = bout./bstd; % trick for printing below
else % find plevels
 draws = [results.bdraw results.pdraw];
 for i=1:results.nvar+1;
 if bout(i,1) > 0
 cnt = find(draws(:,i) > 0);
 tout(i,1) = 1 - (length(cnt)/(results.ndraw-results.nomit));
 else
 cnt = find(draws(:,i) < 0);
 tout(i,1) = 1 - (length(cnt)/(results.ndraw-results.nomit));
 end; % end of if - else
 end; % end of for loop
end; 

y = results.y;
e = y - results.yhat;
sigu = e'*e;
ym = y - mean(y);
rsqr1 = sigu;
rsqr2 = ym'*ym;
rsqr = 1.0 - rsqr1/rsqr2; % conventional r-squared

fprintf(fid,'\n');
fprintf(fid,'Bayesian spatial autoregressive model \n');
if (nflag == 1)
fprintf(fid,'Dependent Variable = %16s \n',vnames(1,:));
end;
fprintf(fid,'R-squared          = %9.4f \n',rsqr);
fprintf(fid,'sigma^2            = %9.4f \n',sige);
if results.rdraw == 0
fprintf(fid,'r-value            = %6d   \n',results.r);
else
fprintf(fid,'mean of rdraws     = %9.4f \n',mean(results.rdraw));
fprintf(fid,'gam(m,k) prior     = %6d,%6d \n',results.m,results.k);
end;  
fprintf(fid,'Nobs, Nvars        = %6d,%6d \n',results.nobs,results.nvar);
fprintf(fid,'ndraws,nomit       = %6d,%6d \n',results.ndraw,results.nomit);
fprintf(fid,'total time in secs = %9.4f   \n',results.time);
if results.time1 ~= 0
fprintf(fid,'time for eigs      = %9.4f \n',results.time1);
end;
if results.time2 ~= 0
fprintf(fid,'time for lndet     = %9.4f \n',results.time2);
end;
if results.time3 ~= 0
fprintf(fid,'time for sampling  = %9.4f \n',results.time3);
end;

if results.lflag == 0
fprintf(fid,'No lndet approximation used \n');
end;
% put in information regarding Pace and Barry approximations
if results.lflag == 1
fprintf(fid,'Pace and Barry, 1999 MC lndet approximation used \n');
fprintf(fid,'order for MC appr  = %6d  \n',results.order);
fprintf(fid,'iter  for MC appr  = %6d  \n',results.iter);
end;
if results.lflag == 2
fprintf(fid,'Pace and Barry, 1998 spline lndet approximation used \n');
end;

fprintf(fid,'min and max rho    = %9.4f,%9.4f \n',results.rmin,results.rmax);
fprintf(fid,'***************************************************************\n');

vstring = 'Variable';
bstring = 'Prior Mean';
tstring = 'Std Deviation';

tmp = [results.bmean results.bstd];

cnames = strvcat(bstring,tstring);
rnames = vstring;
for i=1:nvar
rnames = strvcat(rnames,Vname(i+1,:));
end;

pin.fmt = '%16.6f';
pin.fid = fid;
pin.cnames = cnames;
pin.rnames = rnames;

mprint(tmp,pin);
fprintf(fid,'***************************************************************\n');
fprintf(fid,'      Posterior Estimates \n');

 if strcmp(results.tflag,'tstat')
% now print coefficient estimates, t-statistics and probabilities
tout = norm_prb(results.tstat); % find asymptotic z (normal) probabilities
      
tmp = [bout results.tstat tout];  % matrix to be printed
% column labels for printing results
bstring = 'Coefficient'; tstring = 'Asymptot t-stat'; pstring = 'z-probability';
cnames = strvcat(bstring,tstring,pstring);
in.cnames = cnames;
in.rnames = Vname;
in.fmt = '%16.6f';
in.fid = fid;
mprint(tmp,in);
 else % use p-levels for Bayesian results
tmp = [bout bstd tout];  % matrix to be printed
% column labels for printing results
bstring = 'Coefficient'; tstring = 'Std Deviation'; pstring = 'p-level';
cnames = strvcat(bstring,tstring,pstring);
in.cnames = cnames;
in.rnames = Vname;
in.fmt = '%16.6f';
in.fid = fid;
mprint(tmp,in);
end;

return;

% <=================== end of sar_g case



case {'sart_g','sart_gc'} % <=================== Gibbs spatial autoregressive Tobit model

nobs = results.nobs;
nvar = results.nvar;


% find posterior means
tmp1 = mean(results.bdraw);
pout = mean(results.pdraw);
bout = [tmp1'
        pout];

y = results.y;
yhat = results.yhat;
sige = mean(results.sdraw);
tmp1 = std(results.bdraw);
tmp2 = std(results.pdraw);
bstd = [tmp1'
        tmp2];

if strcmp(results.tflag,'tstat')
 tstat = bout./bstd;
 % find t-stat marginal probabilities
 tout = tdis_prb(tstat,results.nobs);
 results.tstat = bout./bstd; % trick for printing below
else % find plevels
 draws = [results.bdraw results.pdraw];
 for i=1:results.nvar+1;
 if bout(i,1) > 0
 cnt = find(draws(:,i) > 0);
 tout(i,1) = 1 - (length(cnt)/(results.ndraw-results.nomit));
 else
 cnt = find(draws(:,i) < 0);
 tout(i,1) = 1 - (length(cnt)/(results.ndraw-results.nomit));
 end; % end of if - else
 end; % end of for loop
end; 



fprintf(fid,'\n');
fprintf(fid,'Bayesian spatial autoregressive Tobit model \n');
if (nflag == 1)
fprintf(fid,'Dependent Variable = %16s \n',vnames(1,:));
end;
fprintf(fid,'sigma^2            = %9.4f \n',sige);
if results.rdraw == 0
fprintf(fid,'r-value            = %6d   \n',results.r);
else
fprintf(fid,'mean of rdraws     = %9.4f \n',mean(results.rdraw));
fprintf(fid,'gam(m,k) prior     = %6d,%6d \n',results.m,results.k);
end; 
fprintf(fid,'Nobs, Nvars        = %6d,%6d \n',results.nobs,results.nvar);
fprintf(fid,'# censored values  = %6d \n',results.nobsc);
fprintf(fid,'ndraws,nomit       = %6d,%6d \n',results.ndraw,results.nomit);
fprintf(fid,'time in secs       = %9.4f   \n',results.time);
fprintf(fid,'min and max rho    = %9.4f,%9.4f \n',results.rmin,results.rmax);
fprintf(fid,'***************************************************************\n');

vstring = 'Variable';
bstring = 'Prior Mean';
tstring = 'Std Deviation';

tmp = [results.bmean results.bstd];

cnames = strvcat(bstring,tstring);
rnames = vstring;
for i=1:nvar
rnames = strvcat(rnames,Vname(i+1,:));
end;

pin.fmt = '%16.6f';
pin.fid = fid;
pin.cnames = cnames;
pin.rnames = rnames;

mprint(tmp,pin);
fprintf(fid,'***************************************************************\n');
fprintf(fid,'      Posterior Estimates \n');

 if strcmp(results.tflag,'tstat')
% now print coefficient estimates, t-statistics and probabilities
tout = norm_prb(results.tstat); % find asymptotic z (normal) probabilities
      
tmp = [bout results.tstat tout];  % matrix to be printed
% column labels for printing results
bstring = 'Coefficient'; tstring = 'Asymptot t-stat'; pstring = 'z-probability';
cnames = strvcat(bstring,tstring,pstring);
in.cnames = cnames;
in.rnames = Vname;
in.fmt = '%16.6f';
in.fid = fid;
mprint(tmp,in);
 else % use p-levels for Bayesian results
tmp = [bout bstd tout];  % matrix to be printed
% column labels for printing results
bstring = 'Coefficient'; tstring = 'Std Deviation'; pstring = 'p-level';
cnames = strvcat(bstring,tstring,pstring);
in.cnames = cnames;
in.rnames = Vname;
in.fmt = '%16.6f';
in.fid = fid;
mprint(tmp,in);
end;

return;

% <=================== end of sart_g case


case {'sarp_g','sarp_gc'} % <=================== Gibbs spatial autoregressive Probit model

nobs = results.nobs;
nvar = results.nvar;
  
% find posterior means
tmp1 = mean(results.bdraw);
pout = mean(results.pdraw);
bout = [tmp1'
        pout];

sige = mean(results.sdraw);
tmp1 = std(results.bdraw);
tmp2 = std(results.pdraw);
bstd = [tmp1'
        tmp2];

if strcmp(results.tflag,'tstat')
 tstat = bout./bstd;
 % find t-stat marginal probabilities
 tout = tdis_prb(tstat,results.nobs);
 results.tstat = bout./bstd; % trick for printing below
else % find plevels
 draws = [results.bdraw results.pdraw];
 for i=1:results.nvar+1;
 if bout(i,1) > 0
 cnt = find(draws(:,i) > 0);
 tout(i,1) = 1 - (length(cnt)/(results.ndraw-results.nomit));
 else
 cnt = find(draws(:,i) < 0);
 tout(i,1) = 1 - (length(cnt)/(results.ndraw-results.nomit));
 end; % end of if - else
 end; % end of for loop
end; 


fprintf(fid,'\n');
fprintf(fid,'Bayesian spatial autoregressive Probit model \n');
if (nflag == 1)
fprintf(fid,'Dependent Variable = %16s \n',vnames(1,:));
end;
fprintf(fid,'sigma^2         = %9.4f \n',sige);
if results.rdraw == 0
fprintf(fid,'r-value         = %6d   \n',results.r);
else
fprintf(fid,'mean of rdraws  = %9.4f \n',mean(results.rdraw));
fprintf(fid,'gam(m,k) prior  = %6d,%6d \n',results.m,results.k);
end; 
fprintf(fid,'Nobs, Nvars     = %6d,%6d \n',results.nobs,results.nvar);
fprintf(fid,'# 0, 1 y-values = %6d,%6d \n',results.zip,nobs-results.zip);
fprintf(fid,'ndraws,nomit    = %6d,%6d \n',results.ndraw,results.nomit);
fprintf(fid,'time in secs    = %9.4f   \n',results.time);
fprintf(fid,'min and max rho = %9.4f,%9.4f \n',results.rmin,results.rmax);
fprintf(fid,'***************************************************************\n');


 if strcmp(results.tflag,'tstat')
% now print coefficient estimates, t-statistics and probabilities
tout = norm_prb(results.tstat); % find asymptotic z (normal) probabilities
      
tmp = [bout results.tstat tout];  % matrix to be printed
% column labels for printing results
bstring = 'Coefficient'; tstring = 'Asymptot t-stat'; pstring = 'z-probability';
cnames = strvcat(bstring,tstring,pstring);
in.cnames = cnames;
in.rnames = Vname;
in.fmt = '%16.6f';
in.fid = fid;
mprint(tmp,in);
 else % use p-levels for Bayesian results
tmp = [bout bstd tout];  % matrix to be printed
% column labels for printing results
bstring = 'Coefficient'; tstring = 'Std Deviation'; pstring = 'p-level';
cnames = strvcat(bstring,tstring,pstring);
in.cnames = cnames;
in.rnames = Vname;
in.fmt = '%16.6f';
in.fid = fid;
mprint(tmp,in);
end;

return;

% <=================== end of sarp_g case


case {'far'} % <=================== first-order autoregressive model

nvar = 1;
nobs = results.nobs;


% special handling of vnames
Vname = 'Variable';
% add spatial rho parameter name
    Vname = strvcat(Vname,'rho');

fprintf(fid,'\n');
fprintf(fid,'First-order spatial autoregressive model Estimates \n');
if (nflag == 1)
fprintf(fid,'Dependent Variable = %16s \n',vnames(1,:));
end;
fprintf(fid,'R-squared          = %9.4f \n',results.rsqr);
fprintf(fid,'sigma^2            = %9.4f \n',results.sige);
fprintf(fid,'log-likelihood     = %16.8g \n',results.lik);
fprintf(fid,'Nobs, Nvars        = %6d,%3d \n',results.nobs,results.nvar);
fprintf(fid,'# of iterations    = %6d   \n',results.iter);

% print timing information
fprintf(fid,'total time in secs = %9.4f \n',results.time);
fprintf(fid,'time for optimiz   = %9.4f \n',results.time4);
if results.time1 ~= 0
fprintf(fid,'time for lndet     = %9.4f \n',results.time1);
end;
if results.time2 ~= 0
fprintf(fid,'time for eigs      = %9.4f \n',results.time2);
end;
if results.time3 ~= 0
fprintf(fid,'time for t-stat    = %9.4f \n',results.time3);
end;
% print min and max rho used
fprintf(fid,'min and max rho    = %8.4f,%8.4f \n',results.rmin,results.rmax);

if results.lflag == 0
fprintf(fid,'No lndet approximation used \n');
end;
% put in information regarding Pace and Barry approximations
if results.lflag == 1
fprintf(fid,'Pace and Barry, 1999 MC lndet approximation used \n');
fprintf(fid,'order for MC appr  = %6d  \n',results.order);
fprintf(fid,'iter  for MC appr  = %6d  \n',results.liter);
end;
if results.lflag == 2
fprintf(fid,'Pace and Barry, 1998 spline lndet approximation used \n');
end;

fprintf(fid,'***************************************************************\n');

bout = results.rho;

% <=================== end of far case

case {'far_g','far_gc'} % <=================== Gibbs first-order autoregressive model

nobs = results.nobs;
nvar = 1;

% special handling of vnames
Vname = 'Variable';
% add spatial rho parameter name
    Vname = strvcat(Vname,'rho');
    
% find posterior means
bout = mean(results.pdraw);
sige = mean(results.sdraw);
bstd = std(results.pdraw);

if strcmp(results.tflag,'tstat')
 tstat = bout./bstd;
 % find t-stat marginal probabilities
 tout = tdis_prb(tstat,results.nobs);
 results.tstat = bout./bstd; % trick for printing below
else % find plevels
 draws = [results.pdraw];
 if bout > 0
 cnt = find(draws > 0);
 tout = 1 - (length(cnt)/(results.ndraw-results.nomit));
 else
 cnt = find(draws < 0);
 tout = 1 - (length(cnt)/(results.ndraw-results.nomit));
 end; % end of if - else
end; 


y = results.y;
e = y - results.yhat;
sigu = e'*e;
ym = y - mean(y);
rsqr1 = sigu;
rsqr2 = ym'*ym;
rsqr = 1.0 - rsqr1/rsqr2; % conventional r-squared

fprintf(fid,'\n');
fprintf(fid,'Bayesian First-order spatial autoregressive model \n');
if (nflag == 1)
fprintf(fid,'Dependent Variable = %16s \n',vnames(1,:));
end;
fprintf(fid,'R-squared          = %9.4f   \n',rsqr);
fprintf(fid,'sigma^2            = %9.4f   \n',sige);
if results.rdraw ~= 0
fprintf(fid,'rmean              = %9.4f \n',mean(results.rdraw));
else
fprintf(fid,'r-value            = %6d  \n',results.r);
end;
fprintf(fid,'Nobs, Nvars        = %6d,%3d \n',results.nobs,nvar);
fprintf(fid,'ndraws,nomit       = %6d,%6d \n',results.ndraw,results.nomit);
fprintf(fid,'min and max rho    = %9.4f,%9.4f \n',results.rmin,results.rmax);

% print timing information
fprintf(fid,'total time in secs = %9.4f \n',results.time);
if results.time1 ~= 0
fprintf(fid,'time for lndet     = %9.4f \n',results.time1);
end;
if results.time2 ~= 0
fprintf(fid,'time for eigs      = %9.4f \n',results.time2);
end;
if results.time3 ~= 0
fprintf(fid,'time for sampling  = %9.4f \n',results.time3);
end;

if results.lflag == 0
fprintf(fid,'No lndet approximation used \n');
end;
% put in information regarding Pace and Barry approximations
if results.lflag == 1
fprintf(fid,'Pace and Barry, 1999 MC lndet approximation used \n');
fprintf(fid,'order for MC appr  = %6d  \n',results.order);
fprintf(fid,'iter  for MC appr  = %6d  \n',results.liter);
end;
if results.lflag == 2
fprintf(fid,'Pace and Barry, 1998 spline lndet approximation used \n');
end;

fprintf(fid,'***************************************************************\n');


 if strcmp(results.tflag,'tstat')
% now print coefficient estimates, t-statistics and probabilities
tout = norm_prb(results.tstat); % find asymptotic z (normal) probabilities
      
tmp = [bout results.tstat tout];  % matrix to be printed
% column labels for printing results
bstring = 'Coefficient'; tstring = 'Asymptot t-stat'; pstring = 'z-probability';
cnames = strvcat(bstring,tstring,pstring);
in.cnames = cnames;
in.rnames = Vname;
in.fmt = '%16.6f';
in.fid = fid;
mprint(tmp,in);
 else % use p-levels for Bayesian results
tmp = [bout bstd tout];  % matrix to be printed
% column labels for printing results
bstring = 'Coefficient'; tstring = 'Std Deviation'; pstring = 'p-level';
cnames = strvcat(bstring,tstring,pstring);
in.cnames = cnames;
in.rnames = Vname;
in.fmt = '%16.6f';
in.fid = fid;
mprint(tmp,in);
end;

return;

% <=================== end of far_g case

case {'sac'} % <=================== general spatial model

nobs = results.nobs;
nvar = results.nvar;

% special handling of vnames
if ( nflag == 0) %  no variable names supplied, make some up
Vname = 'Variable';
 for i=1:nvar
    tmp = ['variable ',num2str(i)];
    Vname = strvcat(Vname,tmp);
 end;
% add spatial rho, lambda parameter name
    Vname = strvcat(Vname,'rho');
    Vname = strvcat(Vname,'lambda');

elseif (nflag == 1) % the user supplied variable names
Vname = 'Variable';
[tst_n nsize] = size(vnames);
 if tst_n ~= nvar+1
 error('Wrong # of variable names in prt_sem -- check vnames argument');
 end;
 for i=1:nvar
    Vname = strvcat(Vname,vnames(i+1,:));
 end;
% add spatial rho, lambda parameter name
    Vname = strvcat(Vname,'rho');
    Vname = strvcat(Vname,'lambda');

end; % end of nflag issue


fprintf(fid,'\n');
fprintf(fid,'General Spatial Model Estimates \n');
if (nflag == 1)
fprintf(fid,'Dependent Variable = %16s \n',vnames(1,:));
end;
fprintf(fid,'R-squared          = %9.4f \n',results.rsqr);
fprintf(fid,'Rbar-squared       = %9.4f \n',results.rbar);
fprintf(fid,'sigma^2            = %9.4f \n',results.sige);
fprintf(fid,'log-likelihood     = %16.8g \n',results.lik);
fprintf(fid,'Nobs, Nvars        = %6d,%6d \n',results.nobs,results.nvar);
fprintf(fid,'# iterations       = %6d \n',results.iter);
% print timing information
fprintf(fid,'total time in secs = %9.4f \n',results.time);
fprintf(fid,'time for optimiz   = %9.4f \n',results.time4);
if results.time1 ~= 0
fprintf(fid,'time for lndet     = %9.4f \n',results.time1);
end;
if results.time2 ~= 0
fprintf(fid,'time for eigs      = %9.4f \n',results.time2);
end;
if results.time3 ~= 0
fprintf(fid,'time for t-stat    = %9.4f \n',results.time3);
end;
fprintf(fid,'***************************************************************\n');

bout = [results.beta
        results.rho
        results.lam];
        

% <=================== end of sac case

case {'sac_g'} % <=================== Gibbs general spatial model

nobs = results.nobs;
nvar = results.nvar;

% handling of vnames
Vname = 'Variable';
 for i=1:nvar
    tmp = ['variable ',num2str(i)];
    Vname = strvcat(Vname,tmp);
 end;
% add spatial rho parameter name
    Vname = strvcat(Vname,'rho');
% add spatial rho parameter name
    Vname = strvcat(Vname,'lambda');

if (nflag == 1) % the user supplied variable names
Vname = 'Variable';
[tst_n nsize] = size(vnames);
 if tst_n ~= nvar+1
 fprintf(fid,'Wrong # of variable names in prt_sem -- check vnames argument \n');
 fprintf(fid,'will use generic variable names \n');
 nflag = 0;
 else,
 for i=1:nvar
    Vname = strvcat(Vname,vnames(i+1,:));
 end;
% add spatial rho parameter name
    Vname = strvcat(Vname,'rho');
    Vname = strvcat(Vname,'lambda');
 end; % end of if-else
end; % end of nflag issue

  
% find posterior means
tmp1 = mean(results.bdraw);
pout = mean(results.pdraw);
lout = mean(results.ldraw);
bout = [tmp1'
        pout
        lout];

y = results.y;
sige = mean(results.sdraw);
tmp1 = std(results.bdraw);
tmp2 = std(results.pdraw);
tmp3 = std(results.ldraw);
bstd = [tmp1'
        tmp2
        tmp3];

if strcmp(results.tflag,'tstat')
 tstat = bout./bstd;
 % find t-stat marginal probabilities
 tout = tdis_prb(tstat,results.nobs);
 results.tstat = bout./bstd; % trick for printing below
else % find plevels
 draws = [results.bdraw results.pdraw results.ldraw];
 for i=1:results.nvar+2;
 if bout(i,1) > 0
 cnt = find(draws(:,i) > 0);
 tout(i,1) = 1 - (length(cnt)/(results.ndraw-results.nomit));
 else
 cnt = find(draws(:,i) < 0);
 tout(i,1) = 1 - (length(cnt)/(results.ndraw-results.nomit));
 end; % end of if - else
 end; % end of for loop
end; 

e = y - results.yhat;
sigu = e'*e;
ym = y - mean(y);
rsqr1 = sigu;
rsqr2 = ym'*ym;
rsqr = 1.0 - rsqr1/rsqr2; % conventional r-squared

fprintf(fid,'\n');
fprintf(fid,'Bayesian general spatial model \n');
if (nflag == 1)
fprintf(fid,'Dependent Variable = %16s \n',vnames(1,:));
end;
fprintf(fid,'R-squared           = %9.4f \n',rsqr);
fprintf(fid,'sigma^2             = %9.4f \n',sige);
if results.rdraw == 0
fprintf(fid,'r-value             = %6d   \n',results.r);
else
fprintf(fid,'mean of rdraws      = %9.4f \n',mean(results.rdraw));
fprintf(fid,'gam(m,k) prior      = %6d,%6d \n',results.m,results.k);
end;    
fprintf(fid,'Nobs, Nvars         = %6d,%6d \n',results.nobs,results.nvar);
fprintf(fid,'ndraws,nomit        = %6d,%6d \n',results.ndraw,results.nomit);
fprintf(fid,'time in secs        = %9.4f   \n',results.time);
fprintf(fid,'min and max rho     = %9.4f,%9.4f \n',results.rmin,results.rmax);
fprintf(fid,'min and max lambda  = %9.4f,%9.4f \n',results.lmin,results.lmax);
fprintf(fid,'***************************************************************\n');

vstring = 'Variable';
bstring = 'Prior Mean';
tstring = 'Std Deviation';

tmp = [results.bmean results.bstd];

cnames = strvcat(bstring,tstring);
rnames = vstring;
for i=1:nvar
rnames = strvcat(rnames,Vname(i+1,:));
end;
pin.fmt = '%16.6f';
pin.fid = fid;
pin.cnames = cnames;
pin.rnames = rnames;

mprint(tmp,pin);
fprintf(fid,'***************************************************************\n');
fprintf(fid,'      Posterior Estimates \n');

 if strcmp(results.tflag,'tstat')
% now print coefficient estimates, t-statistics and probabilities
tout = norm_prb(results.tstat); % find asymptotic z (normal) probabilities
      
tmp = [bout results.tstat tout];  % matrix to be printed
% column labels for printing results
bstring = 'Coefficient'; tstring = 'Asymptot t-stat'; pstring = 'z-probability';
cnames = strvcat(bstring,tstring,pstring);
in.cnames = cnames;
in.rnames = Vname;
in.fmt = '%16.6f';
in.fid = fid;
mprint(tmp,in);
 else % use p-levels for Bayesian results
tmp = [bout bstd tout];  % matrix to be printed
% column labels for printing results
bstring = 'Coefficient'; tstring = 'Std Deviation'; pstring = 'p-level';
cnames = strvcat(bstring,tstring,pstring);
in.cnames = cnames;
in.rnames = Vname;
in.fmt = '%16.6f';
in.fid = fid;
mprint(tmp,in);
end;

return;

% end of sac_g case

case {'sact_g'} % <=================== Gibbs general spatial Tobit model

nobs = results.nobs;
nvar = results.nvar;

% handling of vnames
Vname = 'Variable';
 for i=1:nvar
    tmp = ['variable ',num2str(i)];
    Vname = strvcat(Vname,tmp);
 end;
% add spatial rho parameter name
    Vname = strvcat(Vname,'rho');
% add spatial rho parameter name
    Vname = strvcat(Vname,'lambda');

if (nflag == 1) % the user supplied variable names
Vname = 'Variable';
[tst_n nsize] = size(vnames);
 if tst_n ~= nvar+1
 fprintf(fid,'Wrong # of variable names in prt_sem -- check vnames argument \n');
 fprintf(fid,'will use generic variable names \n');
 nflag = 0;
 else,
 for i=1:nvar
    Vname = strvcat(Vname,vnames(i+1,:));
 end;
% add spatial rho parameter name
    Vname = strvcat(Vname,'rho');
    Vname = strvcat(Vname,'lambda');
 end; % end of if-else
end; % end of nflag issue

  
% find posterior means
tmp1 = mean(results.bdraw);
pout = mean(results.pdraw);
lout = mean(results.ldraw);
bout = [tmp1'
        pout
        lout];

y = results.y;
sige = mean(results.sdraw);
tmp1 = std(results.bdraw);
tmp2 = std(results.pdraw);
tmp3 = std(results.ldraw);
bstd = [tmp1'
        tmp2
        tmp3];

if strcmp(results.pflag,'tstat')
 tstat = bout./bstd;
 % find t-stat marginal probabilities
 tout = tdis_prb(tstat,results.nobs);
 results.tstat = bout./bstd; % trick for printing below
else % find plevels
 draws = [results.bdraw results.pdraw results.ldraw];
 for i=1:results.nvar+2;
 if bout(i,1) > 0
 cnt = find(draws(:,i) > 0);
 tout(i,1) = 1 - (length(cnt)/(results.ndraw-results.nomit));
 else
 cnt = find(draws(:,i) < 0);
 tout(i,1) = 1 - (length(cnt)/(results.ndraw-results.nomit));
 end; % end of if - else
 end; % end of for loop
end; 

yhat = results.yhat;
e = y - yhat;
sigu = e'*e;
ym = y - mean(y);
rsqr1 = sigu;
rsqr2 = ym'*ym;
rsqr = 1.0 - rsqr1/rsqr2; % conventional r-squared
e = results.ymean - yhat;
sigu = e'*e;
ym = results.ymean - mean(results.ymean);
rsqr1 = sigu;
rsqr2 = ym'*ym;
rsqri = 1.0 - rsqr1/rsqr2; % non-conventional r-squared

fprintf(fid,'\n');
fprintf(fid,'Bayesian general spatial Tobit model \n');
if (nflag == 1)
fprintf(fid,'Dependent Variable = %16s \n',vnames(1,:));
end;
fprintf(fid,'R-squared          = %9.4f \n',rsqr);
fprintf(fid,'Imputed R-squared  = %9.4f \n',rsqri);
fprintf(fid,'sigma^2            = %9.4f \n',sige);
if results.rdraw == 0
fprintf(fid,'r-value            = %6d   \n',results.r);
else
fprintf(fid,'mean of rdraws     = %9.4f \n',mean(results.rdraw));
fprintf(fid,'gam(m,k) prior     = %6d,%6d \n',results.m,results.k);
end;    
fprintf(fid,'Nobs, Nvars        = %6d,%6d \n',results.nobs,results.nvar);
fprintf(fid,'# censored values  = %6d \n',results.nobsc);
fprintf(fid,'ndraws,nomit       = %6d,%6d \n',results.ndraw,results.nomit);
fprintf(fid,'accept rho rate    = %9.4f \n',results.acceptr);
fprintf(fid,'accept lam rate    = %9.4f \n',results.acceptl);
fprintf(fid,'time in secs       = %9.4f   \n',results.time);
fprintf(fid,'min and max rho    = %9.4f,%9.4f \n',results.rmin,results.rmax);
fprintf(fid,'min and max lambda = %9.4f,%9.4f \n',results.lmin,results.lmax);
fprintf(fid,'***************************************************************\n');

vstring = 'Variable';
bstring = 'Prior Mean';
tstring = 'Std Deviation';

tmp = [results.bmean results.bstd];

cnames = strvcat(bstring,tstring);
rnames = vstring;
for i=1:nvar
rnames = strvcat(rnames,Vname(i+1,:));
end;
pin.fmt = '%16.6f';
pin.fid = fid;
pin.cnames = cnames;
pin.rnames = rnames;

mprint(tmp,pin);
fprintf(fid,'***************************************************************\n');
fprintf(fid,'      Posterior Estimates \n');

 if strcmp(results.pflag,'tstat')
% now print coefficient estimates, t-statistics and probabilities
tout = norm_prb(results.tstat); % find asymptotic z (normal) probabilities
      
tmp = [bout results.tstat tout];  % matrix to be printed
% column labels for printing results
bstring = 'Coefficient'; tstring = 'Asymptot t-stat'; pstring = 'z-probability';
cnames = strvcat(bstring,tstring,pstring);
in.cnames = cnames;
in.rnames = Vname;
in.fmt = '%16.6f';
in.fid = fid;
mprint(tmp,in);
 else % use p-levels for Bayesian results
tmp = [bout bstd tout];  % matrix to be printed
% column labels for printing results
bstring = 'Coefficient'; tstring = 'Std Deviation'; pstring = 'p-level';
cnames = strvcat(bstring,tstring,pstring);
in.cnames = cnames;
in.rnames = Vname;
in.fmt = '%16.6f';
in.fid = fid;
mprint(tmp,in);
end;

return;

% end of sact_g case

case {'sacp_g'} % <=================== Gibbs general spatial Probit model

nobs = results.nobs;
nvar = results.nvar;

% handling of vnames
Vname = 'Variable';
 for i=1:nvar
    tmp = ['variable ',num2str(i)];
    Vname = strvcat(Vname,tmp);
 end;
% add spatial rho parameter name
    Vname = strvcat(Vname,'rho');
% add spatial rho parameter name
    Vname = strvcat(Vname,'lambda');

if (nflag == 1) % the user supplied variable names
Vname = 'Variable';
[tst_n nsize] = size(vnames);
 if tst_n ~= nvar+1
 fprintf(fid,'Wrong # of variable names in prt_sem -- check vnames argument \n');
 fprintf(fid,'will use generic variable names \n');
 nflag = 0;
 else,
 for i=1:nvar
    Vname = strvcat(Vname,vnames(i+1,:));
 end;
% add spatial rho parameter name
    Vname = strvcat(Vname,'rho');
    Vname = strvcat(Vname,'lambda');
 end; % end of if-else
end; % end of nflag issue

% find posterior means
tmp1 = mean(results.bdraw);
pout = mean(results.pdraw);
lout = mean(results.ldraw);
bout = [tmp1'
        pout
        lout];

y = results.y;
sige = mean(results.sdraw);
tmp1 = std(results.bdraw);
tmp2 = std(results.pdraw);
tmp3 = std(results.ldraw);
bstd = [tmp1'
        tmp2
        tmp3];

if strcmp(results.pflag,'tstat')
 tstat = bout./bstd;
 % find t-stat marginal probabilities
 tout = tdis_prb(tstat,results.nobs);
 results.tstat = bout./bstd; % trick for printing below
else % find plevels
 draws = [results.bdraw results.pdraw results.ldraw];
 for i=1:results.nvar+2;
 if bout(i,1) > 0
 cnt = find(draws(:,i) > 0);
 tout(i,1) = 1 - (length(cnt)/(results.ndraw-results.nomit));
 else
 cnt = find(draws(:,i) < 0);
 tout(i,1) = 1 - (length(cnt)/(results.ndraw-results.nomit));
 end; % end of if - else
 end; % end of for loop
end; 

yhat = results.yhat;

fprintf(fid,'\n');
fprintf(fid,'Bayesian general spatial Probit model \n');
if (nflag == 1)
fprintf(fid,'Dependent Variable = %16s \n',vnames(1,:));
end;
fprintf(fid,'McFadden R^2    = %9.4f \n',results.r2mf);
fprintf(fid,'Estrella R^2    = %9.4f \n',results.rsqr);
fprintf(fid,'sigma^2         = %9.4f \n',sige);
if results.rdraw == 0
fprintf(fid,'r-value         = %6d   \n',results.r);
else
fprintf(fid,'mean of rdraws  = %9.4f \n',mean(results.rdraw));
fprintf(fid,'gam(m,k) prior  = %6d,%6d \n',results.m,results.k);
end; 
fprintf(fid,'Nobs, Nvars     = %6d,%6d \n',results.nobs,results.nvar);
fprintf(fid,'# 0, 1 y-values = %6d,%6d \n',results.zip,nobs-results.zip);
fprintf(fid,'ndraws,nomit    = %6d,%6d \n',results.ndraw,results.nomit);
fprintf(fid,'rho accept rate = %9.4f \n',results.acceptr);
fprintf(fid,'lam accept rate = %9.4f \n',results.acceptl);
fprintf(fid,'time in secs    = %9.4f   \n',results.time);
fprintf(fid,'min and max rho = %9.4f,%9.4f \n',results.rmin,results.rmax);
fprintf(fid,'***************************************************************\n');

vstring = 'Variable';
bstring = 'Prior Mean';
tstring = 'Std Deviation';

tmp = [results.bmean results.bstd];

cnames = strvcat(bstring,tstring);
rnames = vstring;
for i=1:nvar
rnames = strvcat(rnames,Vname(i+1,:));
end;
pin.fmt = '%16.6f';
pin.fid = fid;
pin.cnames = cnames;
pin.rnames = rnames;

mprint(tmp,pin);
fprintf(fid,'***************************************************************\n');
fprintf(fid,'      Posterior Estimates \n');

 if strcmp(results.pflag,'tstat')
% now print coefficient estimates, t-statistics and probabilities
tout = norm_prb(results.tstat); % find asymptotic z (normal) probabilities
      
tmp = [bout results.tstat tout];  % matrix to be printed
% column labels for printing results
bstring = 'Coefficient'; tstring = 'Asymptot t-stat'; pstring = 'z-probability';
cnames = strvcat(bstring,tstring,pstring);
in.cnames = cnames;
in.rnames = Vname;
in.fmt = '%16.6f';
in.fid = fid;
mprint(tmp,in);
 else % use p-levels for Bayesian results
tmp = [bout bstd tout];  % matrix to be printed
% column labels for printing results
bstring = 'Coefficient'; tstring = 'Std Deviation'; pstring = 'p-level';
cnames = strvcat(bstring,tstring,pstring);
in.cnames = cnames;
in.rnames = Vname;
in.fmt = '%16.6f';
in.fid = fid;
mprint(tmp,in);
 end;

 return;
 
% <=================== end of sacp_g case



case{'moran'}
fprintf(fid,'Moran I-test for spatial correlation in residuals \n');
variable = ' '; 
in.rnames = strvcat(variable,'Moran I','Moran I-statistic','Marginal Probability', ...
         'mean','standard deviation');
in.fmt = '%16.8f';
tmp = sqrt(results.ivar);
mat = [results.morani
       results.istat
       results.prob
       results.imean
       tmp];
mprint(mat,in);
return;    

case{'lratios'}
fprintf(fid,'LR tests for spatial correlation in residuals \n');
variable = ' ';
in.rnames = strvcat(variable,'LR value','Marginal Probability','chi-squared(1) value');
in.fmt = '%16.8f';
mat = [results.lratio
       results.prob
       results.chi1];
mprint(mat,in);
return;    

case{'lmerror'}
fprintf(fid,'LM error tests for spatial correlation in residuals \n');
variable = ' ';
in.rnames = strvcat(variable,'LM value','Marginal Probability','chi(1) .01 value');
in.fmt = '%16.8f';
mat = [results.lm
       results.prob
       results.chi1];
mprint(mat,in);
return;    

case{'lmsar'}
fprintf(fid,'LM error tests for spatial correlation in SAR model residuals \n');
variable = ' ';
in.rnames = strvcat(variable,'LM value','Marginal Probability','chi(1) .01 value');
in.fmt = '%16.8f';
mat = [results.lm
       results.prob
       results.chi1];
mprint(mat,in);
return;    

case{'walds'}
fprintf(fid,'Wald test for spatial correlation in residuals \n');
variable = ' ';
in.rnames = strvcat(variable,'Wald value','Marginal Probability','chi(1) .01 value');
in.fmt = '%16.8f';
mat = [results.wald
       results.prob
       results.chi1];
mprint(mat,in);
return;    

otherwise
error('results structure not known by prt_sem function');
end;


% now print coefficient estimates, t-statistics and probabilities
tout = norm_prb(results.tstat); % find asymptotic z (normal) probabilities
tmp = [bout results.tstat tout];  % matrix to be printed
% column labels for printing results
bstring = 'Coefficient'; tstring = 'Asymptot t-stat'; pstring = 'z-probability';
cnames = strvcat(bstring,tstring,pstring);
in.cnames = cnames;
in.rnames = Vname;
in.fmt = '%16.6f';
in.fid = fid;
mprint(tmp,in);