Categories &

Functions List

Function Reference: unifit

statistics: paramhat = unifit (x)
statistics: [paramhat, paramci] = unifit (x)
statistics: [paramhat, paramci] = unifit (x, alpha)
statistics: [paramhat, paramci] = unifit (x, alpha, freq)

Estimate parameter and confidence intervals for the continuous uniform distribution.

paramhat = unifit (x) returns the maximum likelihood estimate (MLE) of the parameters a and b of the continuous uniform distribution given the data in x. x must be a vector.

[paramhat, paramci] = unifit (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.

[…] = unifit (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)).

Further information about the continuous uniform distribution can be found at https://en.wikipedia.org/wiki/Continuous_uniform_distribution

See also: unifcdf, unifinv, unifpdf, unifrnd, unifstat

Source Code: unifit

Example: 1

  

 ## Sample 2 populations from different continuous uniform distibutions
 rand ("seed", 5);    # for reproducibility
 r1 = unifrnd (2, 5, 2000, 1);
 rand ("seed", 6);    # for reproducibility
 r2 = unifrnd (3, 9, 2000, 1);
 r = [r1, r2];

 ## Plot them normalized and fix their colors
 hist (r, 0:0.5:10, 2);
 h = findobj (gca, "Type", "patch");
 set (h(1), "facecolor", "c");
 set (h(2), "facecolor", "g");
 hold on

 ## Estimate their probability of success
 a_bA = unifit (r(:,1));
 a_bB = unifit (r(:,2));

 ## Plot their estimated PDFs
 x = [0:10];
 y = unifpdf (x, a_bA(1), a_bA(2));
 plot (x, y, "-pg");
 y = unifpdf (x, a_bB(1), a_bB(2));
 plot (x, y, "-sc");
 xlim ([1, 10])
 ylim ([0, 0.5])
 legend ({"Normalized HIST of sample 1 with a=2 and b=5", ...
          "Normalized HIST of sample 2 with a=3 and b=9", ...
          sprintf("PDF for sample 1 with estimated a=%0.2f and b=%0.2f", ...
                  a_bA(1), a_bA(2)), ...
          sprintf("PDF for sample 2 with estimated a=%0.2f and b=%0.2f", ...
                  a_bB(1), a_bB(2))})
 title ("Two population samples from different continuous uniform distibutions")
 hold off
plotted figure