Negative Binomial CDF
p = distfun_nbincdf(x,R,P) p = distfun_nbincdf(x,R,P,lowertail)
a matrix of doubles, the extra trials for R successes, in the set {0,1,2,3,...}.
a matrix of doubles, the number of successes. R belongs to the set {0,1,2,3,4,.......}
a matrix of doubles, the probability of getting success in a Bernoulli trial. P in [0,1].
a 1-by-1 matrix of booleans, the tail (default lowertail=%t). If lowertail is true (the default), then considers P(X<=x) otherwise P(X>x).
a matrix of doubles, the probability.
Computes the cumulative distribution function of the Negative Binomial distribution function.
Any scalar input argument is expanded to a matrix of doubles of the same size as the other input arguments.
// Check with x scalar, R scalar, P scalar computed = distfun_nbincdf(10,4,0.5) expected = 0.9713135 // Check with expanded x computed = distfun_nbincdf([5 10],4,0.5) expected = [0.7460937 0.9713135] // Check with expanded R computed = distfun_nbincdf(5,[5 10],0.5) expected = [0.6230469 0.1508789] // Check with expanded P computed = distfun_nbincdf(5,4,[0.5 0.7]) expected = [0.7460937 0.9747052] // Check with two arguments expanded computed = distfun_nbincdf([5 10],[5 7],0.5) expected = [0.6230469 0.8338470] // Check with all the arguments expanded computed = distfun_nbincdf([5 10],[4 7],[0.5 0.6]) expected = [0.7460937 0.9651873] //Plot the function scf(); x = (0:20)'; p1=distfun_nbincdf(x,20,0.5); p2=distfun_nbincdf(x,20,0.7); p3=distfun_nbincdf(x,40,0.5); legendspec=["P=0.5, N=20","P=0.7, N=20","P=0.5, N=40"]; distfun_plotintcdf(x,[p1,p2,p3],["b" "g" "r"],legendspec); xtitle("Negative Binomial CDF") //check upper tail p = distfun_nbincdf(3,5,0.5) lt_expected = 0.3632813 q = distfun_nbincdf(3,5,0.5,%f) ut_expected = 0.6367187 | ![]() | ![]() |
http://en.wikipedia.org/wiki/Binomial_distribution