function [p,plo,pup] = normcdf_my(x,mu,sigma,pcov,alpha)
%NORMCDF Normal cumulative distribution function (cdf).
%   P = NORMCDF(X,MU,SIGMA) returns the cdf of the normal distribution with
%   mean MU and standard deviation SIGMA, evaluated at the values in X.
%   The size of P is the common size of X, MU and SIGMA.  A scalar input
%   functions as a constant matrix of the same size as the other inputs.
%
%   Default values for MU and SIGMA are 0 and 1, respectively.
%
%   [P,PLO,PUP] = NORMCDF(X,MU,SIGMA,PCOV,ALPHA) produces confidence bounds
%   for P when the input parameters MU and SIGMA are estimates.  PCOV is a
%   2-by-2 matrix containing the covariance matrix of the estimated parameters.
%   ALPHA has a default value of 0.05, and specifies 100*(1-ALPHA)% confidence
%   bounds.  PLO and PUP are arrays of the same size as P containing the lower
%   and upper confidence bounds.
%
%   See also ERF, ERFC, NORMFIT, NORMINV, NORMLIKE, NORMPDF, NORMRND, NORMSTAT.
 
%   References:
%      [1] Abramowitz, M. and Stegun, I.A. (1964) Handbook of Mathematical
%          Functions, Dover, New York, 1046pp., sections 7.1, 26.2.
%      [2] Evans, M., Hastings, N., and Peacock, B. (1993) Statistical
%          Distributions, 2nd ed., Wiley, 170pp.
 
%   Copyright 1993-2004 The MathWorks, Inc. 
%   $Revision: 2.16.4.2 $  $Date: 2004/08/20 20:06:02 $
 
if nargin<1
    error('Input argument X is undefined.');
end
if nargin < 2
    mu = 0;
end
if nargin < 3
    sigma = 1;
end
  
% Return NaN for out of range parameters.
sigma(sigma <= 0) = NaN;
 
try
    z = (x-mu) ./ sigma;
catch
    error('Non-scalar arguments must match in size.');
end
 
% Use the complementary error function, rather than .5*(1+erf(z/sqrt(2))),
% to produce accurate near-zero results for large negative x.
p = 0.5 * erfc(-z ./ sqrt(2));
 
% Compute confidence bounds if requested.
if nargout>=2
   zvar = (pcov(1,1) + 2*pcov(1,2)*z + pcov(2,2)*z.^2) ./ (sigma.^2);;
   if any(zvar<0)
      error('PCOV must be a positive semi-definite matrix.');
   end
   normz = -norminv_my(alpha/2);
   halfwidth = normz * sqrt(zvar);
   zlo = z - halfwidth;
   zup = z + halfwidth;
 
   plo = 0.5 * erfc(-zlo./sqrt(2));
   pup = 0.5 * erfc(-zup./sqrt(2));
end