Kernel density estimation Problem graph ?

l = linspace(-4,4,500);
fhat = zeros(size(l));
h = 1.06*n^(-1/5);
hold on
for i=1:n   % get each kernel function evaluated at x% centered at data   
  f = exp(-(1/(2*h^2))*(l-x(i)).^2)/sqrt(2*pi)/h; 
 plot(l,f/(n*h));  
 fhat = fhat+f/(n);
end
plot(l,fhat);
 hold off 
 
 Thats my code for kernel density estimation where x(i) is dataset of mixture of gaussian and beta distribution but the graph i am getting is value so small that even i cant see it and i am not sure is it right?

Answer 1

[Shubh Sahu]

See this piece of code:

f = exp(-(1/(2*h^2))*(l-x(i)).^2)/sqrt(2*pi)/h; 
 plot(l,f/(n*h));

In this f is a point and l is a vector. So in first place you are plotting point against a vector so it will be very difficult to see. Make fhat an array.

fhat(i)= fhat(i-1)+f/(n);

Note: I will strongly recommend you to see this link. Never plot anything inside for loop until it's very necessary to do that.