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CdfNoncentralF Method
Evaluates the noncentral F cumulative distribution function (CDF).

Namespace: Imsl.Stat
Assembly: ImslCS (in ImslCS.dll) Version: 6.5.2.0
Syntax
public static double NoncentralF(
	double f,
	double df1,
	double df2,
	double lambda
)

Parameters

f
Type: SystemDouble
A double value representing the argument at which the function is to be evaluated. f must be nonnegative.
df1
Type: SystemDouble
A double value representing the number of numerator degrees of freedom. df1 must be positive.
df2
Type: SystemDouble
A double value representing the number of denominator degrees of freedom. df2 must be positive.
lambda
Type: SystemDouble
A double value representing the noncentrality parameter. lambda must be nonnegative.

Return Value

Type: Double
A double scalar value representing the probability that a noncentral F random variable takes a value less than or equal to f.
Remarks

The noncentral F distribution is a generalization of the F distribution. If X is a noncentral chi-square random variable with noncentrality parameter \lambda and \nu_1 degrees of freedom, and Y is a chi-square random variable with \nu_2 degrees of freedom which is statistically independent of X, then


            F \;\; = \;\; \frac{ (X/\nu_1)}{(Y/\nu_2)}
is a noncentral F-distributed random variable whose CDF is given by:

            CDF(f, \nu_1, \nu_2, \lambda) \;\; = \;\; \sum_{j = 0}^\infty {c_j}
where:

            c_j \;\; = \;\; \omega_j \; I_x (\frac{\nu_1}{2} + j, \; \frac{\nu_1}{2})

            \omega_j  \;\; = \;\; e^{-\lambda / 2} \; \frac{(\lambda / 2)^{j}}{j!}
            \;\; = \;\; \frac{\lambda}{2j} \; \omega_{j-1}

            I_x (a, b)  \;\; = \;\; \frac{B_x (a, b)}{B (a, b)}

            B_x (a, b)  \;\; = \;\; \int_{0}^{x} t^{a-1} (1-t)^{b-1} dt \;\; = \;\;
            x^{a} \sum_{j = 0}^\infty {\frac{\Gamma(j+1-b)} {(a+j) \; \Gamma(1-b) \; j!}) \; x^{j}}

            B (a, b)  \;\; = \;\; B_1 (a, b)  \;\; = \;\; \frac{\Gamma(a)  \; \Gamma(b)} {\Gamma(a + b)}

            I_x (a+1, b)  \;\; = \;\; I_x (a, b)  \; - \; T_x (a, b)

            T_x (a, b)  \;\; = \;\; \frac{\Gamma(a + b)}{\Gamma(a+1)  \; \Gamma(b)} x^{a} (1-x)^{b} \;\; = \;\;
            T_x (a-1, b)  \; \frac{a-1+b}{a}  \; x

            x  \;\; = \;\; \frac{\nu_1 f}{\nu_2 \; +  \; \nu_1 f}
and \Gamma (\cdot) is the gamma function, \nu_1 = df1, \nu_2 = df2, \lambda = lambda, and f = f.

With a noncentrality parameter of zero, the noncentral F distribution is the same as the F distribution.

See Also