Statistics: geofit

### Function Reference: `geofit`

statistics: pshat = geofit (x)
statistics: [pshat, psci] = geofit (x)
statistics: [pshat, psci] = geofit (x, alpha)
statistics: [pshat, psci] = geofit (x, alpha, freq)

Estimate parameter and confidence intervals for the geometric distribution.

`pshat = geofit (x)` returns the maximum likelihood estimate (MLE) of the probability of success for the geometric distribution. x must be a vector.

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

`[…] = geofit (x, alpha, 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))`.

The geometric distribution models the number of failures (x) of a Bernoulli trial with probability ps before the first success.

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

See also: geocdf, geoinv, geopdf, geornd, geostat

Source Code: geofit

### Example: 1

     ## Sample 2 populations from different geometric distibutions
     rande ("seed", 1);    # for reproducibility
     r1 = geornd (0.15, 1000, 1);
     rande ("seed", 2);    # for reproducibility
     r2 = geornd (0.5, 1000, 1);
     r = [r1, r2];

     ## Plot them normalized and fix their colors
     hist (r, 0:0.5:20.5, 1);
     h = findobj (gca, "Type", "patch");
     set (h(1), "facecolor", "c");
     set (h(2), "facecolor", "g");
     hold on

     ## Estimate their probability of success
     pshatA = geofit (r(:,1));
     pshatB = geofit (r(:,2));

     ## Plot their estimated PDFs
     x = [0:15];
     y = geopdf (x, pshatA);
     plot (x, y, "-pg");
     y = geopdf (x, pshatB);
     plot (x, y, "-sc");
     xlim ([0, 15])
     ylim ([0, 0.6])
     legend ({"Normalized HIST of sample 1 with ps=0.15", ...
              "Normalized HIST of sample 2 with ps=0.50", ...
              sprintf("PDF for sample 1 with estimated ps=%0.2f", ...
                      mean (pshatA)), ...
              sprintf("PDF for sample 2 with estimated ps=%0.2f", ...
                      mean (pshatB))})
     title ("Two population samples from different geometric distibutions")
     hold off