function prt_reg(results,vnames,fid)
% PURPOSE: Prints output using regression results structures
%---------------------------------------------------
% USAGE: prt_reg(results,vnames,fid)
% Where: results = a structure returned by a 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_reg(results,[],fid) to print to a file with no vnames               
% --------------------------------------------------
%  RETURNS: nothing, just prints the 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_reg 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_reg');
end;

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

% make up some generic variable names
Vname = 'Variable';
 for i=1:nvar
    tmp = ['variable ',num2str(i)];
    Vname = strvcat(Vname,tmp);
 end;
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_reg -- 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;
 end; % end of if-else
end; % end of nflag issue

switch results.meth

case {'arma'} % <=================== box-jenkins regressions

p = length(results.ar);
q = length(results.ma);

fprintf(fid,'\n');
fprintf(fid,'Box-Jenkins ARMA Estimates \n');
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 = %9.4f \n',results.like);
fprintf(fid,'Nobs           = %6d \n',results.nobs);
fprintf(fid,'AR,MA orders   = %6d , %6d \n',p,q);
fprintf(fid,'Iterations     = %6d \n',results.iter);
fprintf(fid,'***************************************************************\n');
Vname = 'Variable';
% create special variable names for box-jenkins
if results.cterm == 0;
 for i=1:nvar
 if i <= p
        tmp = str2mat(['AR ',num2str(i)]);
        Vname = strvcat(Vname,tmp);
     elseif i <= p+q
        tmp = str2mat(['MA ',num2str(i-p)]);
        Vname = strvcat(Vname,tmp);
     end;
 end;
else
 for i=1:nvar
 if i <= p
        tmp = str2mat(['AR ',num2str(i)]);
        Vname = strvcat(Vname,tmp);
    elseif i == p+1
        Vname = strvcat(Vname,'Const');
    else   
        tmp = str2mat(['MA ',num2str(i-p-1)]);
        Vname = strvcat(Vname,tmp);
    end;
 end;
end;

% <=================== end of boxjenkins case

case {'ols','hwhite','nwest'} % <=================== ols,white,nwest regressions

fprintf(fid,'\n');
if strcmp(results.meth,'ols')
fprintf(fid,'Ordinary Least-squares Estimates \n');
elseif strcmp(results.meth,'hwhite')
fprintf(fid,'White Heteroscedastic Consistent Estimates \n');
elseif strcmp(results.meth,'nwest')
fprintf(fid,'Newey-West hetero/serial Consistent Estimates \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,'sigma^2        = %9.4f \n',results.sige);
fprintf(fid,'Durbin-Watson  = %9.4f \n',results.dw);
fprintf(fid,'Nobs, Nvars    = %6d,%6d \n',results.nobs,results.nvar);
fprintf(fid,'***************************************************************\n');

% <=================== end of ols,white, newey-west case

case {'olsc','olsar1'} % <=================== olsc, olsar1 regressions

fprintf(fid,'\n');
if strcmp(results.meth,'olsc')
  fprintf(fid,'Cochrane-Orcutt serial correlation Estimates \n');
elseif strcmp(results.meth,'olsar1');
  fprintf(fid,'Maximum likelihood ar1 serial correlation Estimates \n');
end; 
nobs = results.nobs;
nvar = results.nvar;

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,'Durbin-Watson   = %9.4f \n',results.dw);
fprintf(fid,'Rho estimate    = %9.4f \n',results.rho);
fprintf(fid,'Rho t-statistic = %9.4f \n',results.trho);
fprintf(fid,'Rho probability = %9.4f \n',tdis_prb(results.trho,nobs-nvar));
fprintf(fid,'Nobs, Nvars     = %6d,%6d \n',nobs,nvar);
if strcmp(results.meth,'olsar1')
fprintf(fid,'Iterations      = %6d \n',results.iter);
fprintf(fid,'Log Likelihood  = %16.8g \n',-results.like);
end;
fprintf(fid,'***************************************************************\n');

if strcmp(results.meth,'olsc')
fprintf(fid,'Iteration information \n');
[itmax endr] = size(results.iter);
in.cnames = strvcat('rho value','convergence','iteration');
in.fmt = strvcat('%16.6f','%16.6f','%12d');
mprint(results.iter,in);
fprintf(fid,'***************************************************************\n');

end;

% <=================== end of olsc, olsar1 cases

case {'ridge'} % <=================== ridge regressions

fprintf(fid,'\n');
fprintf(fid,'Ridge Regression 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,'Durbin-Watson  = %9.4f \n',results.dw);
fprintf(fid,'Ridge theta    = %16.8g \n',results.theta);
fprintf(fid,'Nobs, Nvars    = %6d,%6d \n',results.nobs,results.nvar);
fprintf(fid,'***************************************************************\n');

% <=================== end of ridge case

case {'logit','probit'}  % <=================== logit,probit regressions

fprintf(fid,'\n');
if strcmp(results.meth,'logit')
fprintf(fid,'Logit Maximum Likelihood Estimates \n');
elseif strcmp(results.meth,'probit')
fprintf(fid,'Probit Maximum Likelihood Estimates \n');
end;

if (nflag == 1)
fprintf(fid,'Dependent Variable = %16s \n',vnames(1,:));
end;
fprintf(fid,'McFadden R-squared     = %9.4f \n',results.r2mf);
fprintf(fid,'Estrella R-squared     = %9.4f \n',results.rsqr);
fprintf(fid,'LR-ratio, 2*(Lu-Lr)    = %9.4f \n',results.lratio);
fprintf(fid,'LR p-value             = %9.4f \n',1-chis_prb(results.lratio,nvar-1));
fprintf(fid,'Log-Likelihood         = %9.4f \n',results.lik);
fprintf(fid,'# of iterations        = %6d   \n',results.iter);
fprintf(fid,'Convergence criterion  = %16.8g \n',results.convg);
fprintf(fid,'Nobs, Nvars            = %6d,%6d \n',results.nobs,results.nvar);
fprintf(fid,'# of 0''s, # of 1''s     = %6d,%6d \n',results.zip,results.one);
fprintf(fid,'***************************************************************\n');

%<=================== end of logit,probit

case {'mlogit'}  % <=================== mlogit regressions

fprintf(fid,'\n');
fprintf(fid,'Multinomial Logit Maximum Likelihood Estimates \n');

if (nflag == 1)
fprintf(fid,'Dependent Variable = %16s \n',vnames(1,:));
end;
fprintf(fid,'Log-Likelihood         = %9.4f \n',results.lik);
fprintf(fid,'# of iterations        = %6d   \n',results.iter);
fprintf(fid,'Nobs, Nvars            = %6d,%6d \n',results.nobs,results.nvar);
fprintf(fid,'# of categories        = %6d     \n',results.k);
fprintf(fid,'***************************************************************\n');

bstring = 'Gradient';
in.cnames = strvcat(bstring);
in.fmt = '%16.8f';
in.fid = fid;
fprintf(fid,'gradient at solution \n');
mprint(results.grad(1:nvar,1),in);

case {'tobit'} % <=================== tobit, regressions
 
fprintf(fid,'\n');
 fprintf(fid,'Tobit Regression 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,'# iterations   = %6d  \n',results.iter);
fprintf(fid,'optimization   = %6s  \n',results.opt);
fprintf(fid,'Nobs, Nvars    = %6d,%6d \n',results.nobs,results.nvar);
fprintf(fid,'# of censored  = %6d \n',results.nobsc);
fprintf(fid,'***************************************************************\n');
bstring = 'Gradient';
in.cnames = strvcat(bstring);
in.rnames = strvcat(Vname,'sigma');
in.fmt = '%16.8f';
in.fid = fid;
fprintf(fid,'gradient at solution \n');
mprint(results.grad(1:nvar+1,1),in);

% <=================== end of tobit case

case {'theil'} % <=================== theil-goldberger regressions

fprintf(fid,'\n');
fprintf(fid,'Theil-Goldberger Regression 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,'Durbin-Watson  = %9.4f \n',results.dw);
fprintf(fid,'Nobs, Nvars    = %6d,%6d \n',results.nobs,results.nvar);
fprintf(fid,'***************************************************************\n');

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

tmp = [results.pmean results.pstd];

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

case {'robust'} % <=================== robust regressions

fprintf(fid,'\n');
fprintf(fid,'Robust Regression 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,'Weighting meth = %16s   \n',results.wfunc);
fprintf(fid,'Weight param   = %9.4f  \n',results.wparm);
fprintf(fid,'sigma^2        = %9.4f  \n',results.sige);
fprintf(fid,'Durbin-Watson  = %9.4f \n',results.dw);
fprintf(fid,'Nobs, Nvars    = %6d,%6d \n',results.nobs,results.nvar);
fprintf(fid,'# iterations   = %6d \n',results.iter);
fprintf(fid,'converg crit   = %16.8g \n',results.convg);
fprintf(fid,'***************************************************************\n');

case {'olst'} % <=================== regression with t-distributed errors

fprintf(fid,'\n');
fprintf(fid,'Regression with t-distributed errors \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,'Durbin-Watson  = %9.4f \n',results.dw);
fprintf(fid,'Nobs, Nvars    = %6d,%6d \n',results.nobs,results.nvar);
fprintf(fid,'# iterations   = %6d \n',results.iter);
fprintf(fid,'converg crit   = %16.8g \n',results.conv);
fprintf(fid,'***************************************************************\n');

% <=================== end of olst case

case {'tsls'} % <=================== two-stage least-squaes regressions

fprintf(fid,'\n');
fprintf(fid,'Two Stage Least-squares Regression 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,'Durbin-Watson  = %9.4f \n',results.dw);
fprintf(fid,'Nobs, Nvars    = %6d,%6d \n',results.nobs,results.nvar);
fprintf(fid,'***************************************************************\n');

% <=================== end of two-stage least-squares case

case {'lad'} % <=================== least-absolute deviations regression

fprintf(fid,'\n');
fprintf(fid,'Least-Absolute Deviation 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,'Durbin-Watson  = %9.4f \n',results.dw);
fprintf(fid,'Nobs, Nvars    = %6d,%6d \n',results.nobs,results.nvar);
fprintf(fid,'# iterations   = %6d \n',results.iter);
fprintf(fid,'convergence    = %16.8g \n',results.conv);
fprintf(fid,'***************************************************************\n');

case {'boxcox'} % <=================== Box-Cox regression

fprintf(fid,'\n');
fprintf(fid,'Box-Cox 1-parameter 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,'Lambda         = %9.4f \n',results.lam);
fprintf(fid,'Nobs, Nvars    = %6d,%6d \n',results.nobs,results.nvar);
fprintf(fid,'# iterations   = %6d \n',results.iter);
fprintf(fid,'-log like      = %16.8g \n',results.like);
fprintf(fid,'***************************************************************\n');

case {'boxcox2'} % <=================== Box-Cox 2-parameter regression

fprintf(fid,'\n');
fprintf(fid,'Box-Cox 2-parameter 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,'Lam1           = %9.4f \n',results.lam1);
fprintf(fid,'Lam2           = %9.4f \n',results.lam2);
fprintf(fid,'Nobs, Nvars    = %6d,%6d \n',results.nobs,results.nvar);
fprintf(fid,'# iterations   = %6d \n',results.iter);
fprintf(fid,'-log like      = %16.8g \n',results.like);
fprintf(fid,'***************************************************************\n');

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

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