### Function Reference: `evfit`

statistics: paramhat = evfit (x)
statistics: [paramhat, paramci] = evfit (x)
statistics: [paramhat, paramci] = evfit (x, alpha)
statistics: […] = evfit (x, alpha, censor)
statistics: […] = evfit (x, alpha, censor, freq)
statistics: […] = evfit (x, alpha, censor, freq, options)

Estimate parameters and confidence intervals for the extreme value distribution.

`paramhat = evfit (x)` returns the maximum likelihood estimates of the parameters of the extreme value distribution (also known as the Gumbel or the type I generalized extreme value distribution) given the data in x. `paramhat(1)` is the location parameter, mu, and `paramhat(2)` is the scale parameter, sigma.

`[paramhat, paramci] = evfit (x)` returns the 95% confidence intervals for the parameter estimates.

`[…] = evfit (x, alpha)` also returns the `100 * (1 - alpha)` percent confidence intervals for the parameter estimates. By default, the optional argument alpha is 0.05 corresponding to 95% confidence intervals. Pass in `[]` for alpha to use the default values.

`[…] = evfit (x, alpha, censor)` accepts a boolean vector, censor, of the same size as x with `1`s for observations that are right-censored and `0`s for observations that are observed exactly. By default, or if left empty, `censor = zeros (size (x))`.

`[…] = evfit (x, alpha, censor, freq)` accepts a frequency vector, freq, of the same size as x. freq typically contains integer frequencies for the corresponding elements in x, but it can contain any non-integer non-negative values. By default, or if left empty, `freq = ones (size (x))`.

`[…] = evfit (…, options)` specifies control parameters for the iterative algorithm used to compute the maximum likelihood estimates. options is a structure with the following field and its default value:

- `options.Display = "off"`
- `options.MaxFunEvals = 400`
- `options.MaxIter = 200`
- `options.TolX = 1e-6`

The Gumbel distribution is used to model the distribution of the maximum (or the minimum) of a number of samples of various distributions. This version is suitable for modeling minima. For modeling maxima, use the alternative Gumbel fitting function, `gumbelfit`.

Further information about the Gumbel distribution can be found at https://en.wikipedia.org/wiki/Gumbel_distribution

See also: evcdf, evinv, evpdf, evrnd, evlike, evstat, gumbelfit

Source Code: evfit

### Example: 1

     ## Sample 3 populations from different extreme value distibutions
     rand ("seed", 1);    # for reproducibility
     r1 = evrnd (2, 5, 400, 1);
     rand ("seed", 12);    # for reproducibility
     r2 = evrnd (-5, 3, 400, 1);
     rand ("seed", 13);    # for reproducibility
     r3 = evrnd (14, 8, 400, 1);
     r = [r1, r2, r3];

     ## Plot them normalized and fix their colors
     hist (r, 25, 0.4);
     h = findobj (gca, "Type", "patch");
     set (h(1), "facecolor", "c");
     set (h(2), "facecolor", "g");
     set (h(3), "facecolor", "r");
     ylim ([0, 0.28])
     xlim ([-30, 30]);
     hold on

     ## Estimate their MU and SIGMA parameters
     mu_sigmaA = evfit (r(:,1));
     mu_sigmaB = evfit (r(:,2));
     mu_sigmaC = evfit (r(:,3));

     ## Plot their estimated PDFs
     x = [min(r(:)):max(r(:))];
     y = evpdf (x, mu_sigmaA(1), mu_sigmaA(2));
     plot (x, y, "-pr");
     y = evpdf (x, mu_sigmaB(1), mu_sigmaB(2));
     plot (x, y, "-sg");
     y = evpdf (x, mu_sigmaC(1), mu_sigmaC(2));
     plot (x, y, "-^c");
     legend ({"Normalized HIST of sample 1 with μ=2 and σ=5", ...
              "Normalized HIST of sample 2 with μ=-5 and σ=3", ...
              "Normalized HIST of sample 3 with μ=14 and σ=8", ...
              sprintf("PDF for sample 1 with estimated μ=%0.2f and σ=%0.2f", ...
                      mu_sigmaA(1), mu_sigmaA(2)), ...
              sprintf("PDF for sample 2 with estimated μ=%0.2f and σ=%0.2f", ...
                      mu_sigmaB(1), mu_sigmaB(2)), ...
              sprintf("PDF for sample 3 with estimated μ=%0.2f and σ=%0.2f", ...
                      mu_sigmaC(1), mu_sigmaC(2))})
     title ("Three population samples from different extreme value distibutions")
     hold off