evcdf
Extreme value cumulative distribution function (CDF).
For each element of x, compute the cumulative distribution function (CDF) of the type 1 extreme values CDF at x of the normal distribution with location parameter mu and scale parameter sigma. 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 are mu = 0, sigma = 1.
When called with three output arguments, [p, plo,
pup]
it computes the confidence bounds for p when the input
parameters mu and sigma are estimates. In such case, pcov,
a 2-by-2 matrix containing the covariance matrix of the estimated parameters,
is necessary. 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.
The type 1 extreme value distribution is also known as the Gumbel
distribution. The version used here is suitable for modeling minima; the
mirror image of this distribution can be used to model maxima by negating
x. If y has a Weibull distribution, then
x = log (y)
has the type 1 extreme value distribution.
[…] = evcdf (…, "upper")
computes the upper tail
probability of the extreme value distribution.
See also: evinv, evpdf, evrnd, evfit, evlike, evstat
Source Code: evcdf