% DENPLOT.M: procedure to compute univariate kernel density estimator 
%            of a vector of data and then plot results
%            calls kdensity.m to compute kernel density estimate at points 
%            to be plotted
%
%            John Rust, University of Maryland, November, 2011
%   
%  
%	   inputs:
% 
%          vname: string variable, name of variable (in memory) which 
%                 comprises that data to be used to sstimate a density
%          pmin:  lower end point of an interval to plot estimated density
%          pmax:  upper end point of an interval to plot estimated density
%          n:     number of points (equally spaced) between pmin and pmax
%                 at which estimated density is plotted
%
%	   implicit global inputs
%
%	   kerneltyp: type of kernel density to use in the density estimation
%
%	              epanechnikov
%		      gaussian
	   
function denplot(vname,pmin,pmax,n);  
  
       data=evalin('base',vname);

       x=(pmin:(pmax-pmin)/n:pmax)';

       kde=kdensity(x,data);

       plot(x,kde);
       title(sprintf('Kernel density plot of %s',vname));
       axis([pmin pmax 0 max(kde)]);
       ylabel('Density');
       xlabel(vname);
       locfac=.05;
       text(pmin+locfac*(pmax-pmin),max(kde)*.9,['Mean    ' num2str(mean(data))]);
       text(pmin+locfac*(pmax-pmin),max(kde)*.85,['Median  ' num2str(median(data))]);
       text(pmin+locfac*(pmax-pmin),max(kde)*.8,['Minimum ' num2str(min(data))]);
       text(pmin+locfac*(pmax-pmin),max(kde)*.75,['Maximum ' num2str(max(data))]);
       text(pmin+locfac*(pmax-pmin),max(kde)*.7,['Std dev ' num2str(std(data))]);
       text(pmin+locfac*(pmax-pmin),max(kde)*.65,['N       ' num2str(size(data,1))]);