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Dataplot Vol 2 Vol 1

IBCDF

Name:
    IBCDF (LET)
Type:
    Library Function
Purpose:
    Compute the standard inverted beta cumulative distribution function. This is also referred to as the beta distribution of the second kind or the beta prime distribution.
Description:
    The standard inverted beta distribution has the following probability density function:

      f(x,alpha,beta) = x**(alpha-1)/
[B(alpha,beta)*(1+x)**(alpha+beta)]   
x, alpha, beta > 0

    with B denoting the beta function (HELP BETA for details).

    The inverted beta cumulative distribution function is computed by numerically integrating the inverted beta probability density function.

Syntax:
    LET <y> = IBCDF(<x>,<alpha>,<beta>)
                            <SUBSET/EXCEPT/FOR qualification>
    where <x> is a variable, a number, or a parameter;
                <alpha> is a number or parameter specifying the first shape parameter;
                <beta> is a number or parameter specifying the second shape parameter;
                <y> is a variable or a parameter (depending on what <x> is) where the computed inverted beta cdf value is stored;
    and where the <SUBSET/EXCEPT/FOR qualification> is optional.
Examples:
    LET A = IBCDF(3,2,1.5)
    LET X2 = IBCDF(X1,A,B)
    PLOT IBCDF(X,0.5,2) FOR X = 0.01 0.01 10
Note:
    Dataplot uses the DQAGI routine from the Quadpack library to perform the numerical integration.
Default:
    None
Synonyms:
    None
Related Commands:
    IBPDF = Compute the inverted beta probability density function.
    IBPPF = Compute the inverted beta percent point function.
    BETPDF = Compute the beta probability density function.
    NORPDF = Compute the normal probability density function.
    RANDOM NUMBERS = Generate random numbers from 60+ univariate distributions.
Reference:
    "Continuous Univariate Distributions, Volume 2", Second Edition, Johnson, Kotz, and Balakrishnan, Wiley, 1994, p. 248.

    "Statistical Distributions", Third Edition, Evans, Hastings, and Peacock, Wiley, 2000.

Applications:
    Distributional Modeling
Implementation Date:
    2004/1
Program:
     
    LET ALPHA = DATA 0.5 0.5 0.5 1 1 1 2 2 2
    LET BETA = DATA 0.5 1 2 0.5 1 2 0.5 1 2
    .
    MULTIPLOT 3 3
    MULTIPLOT CORNER COORDINATES 0 0 100 100
    MULTIPLOT SCALE FACTOR 3
    Y1LIMITS 0 1
    MAJOR YTIC MARK NUMBER 6
    LOOP FOR K = 1 1 9
        LET A1 = ALPHA(K)
        LET B1 = BETA(K)
        TITLE ALPHA = ^A1, BETA = ^B1
        PLOT IBCDF(X,A1,B1)  FOR X = 0.1  0.1 10
    END OF LOOP
        
    plot generated by sample program

Date created: 2/3/2004
Last updated: 2/3/2004
Please email comments on this WWW page to alan.heckert@nist.gov.