cfX_FisherSnedecor: Characteristic function of the central FISHER-SNEDECOR...

View source: R/cfX_FisherSnedecor.R

cfX_FisherSnedecorR Documentation

Characteristic function of the central FISHER-SNEDECOR F-distribution

Description

cfX_FisherSnedecor(t, df1, df2, coef, niid, tol) evaluates characteristic function of the central FISHER-SNEDECOR F-distribution with df1 > 0 and df2 > 0 degrees of freedom.

cfX_FisherSnedecor is an ALIAS NAME of the more general function cf_FisherSnedecor, used to evaluate the characteristic function of a linear combination of independent FISHER-SNEDECOR F-distributed random variables.

The characteristic function of X ~ F(df1,df2) is defined by cf(t) = U(df1/2, 1-df2/2, -1i*(df2/df1)*t), where U(a,b,z) denotes the confluent hypergeometric function of the second kind.

Usage

cfX_FisherSnedecor(t, df1, df2, coef, niid, tol)

Arguments

t

vector or array of real values, where the CF is evaluated.

df1

vector of the degrees of freedom df1 > 0. If empty, default value is df1 = 1.

df2

vector of the degrees of freedom df2 > 0. If empty, default value is df2 = 1.

coef

vector of the coefficients of the linear combination of the log-transformed random variables. If coef is scalar, it is assumed that all coefficients are equal. If empty, default value is coef = 1.

niid

scalar convolution coeficient niid, such that Z = Y + ... + Y is sum of niid iid random variables Y, where each Y = sum_{i=1}^N coef(i) * log(X_i) is independently and identically distributed random variable. If empty, default value is niid = 1.

tol

tolerance factor for selecting the Poisson weights, i.e. such that PoissProb > tol. If empty, default value is tol = 1e-12.

Value

Characteristic function cf(t) of the central FISHER-SNEDECOR F-distribution.

Note

Ver.: 06-Oct-2018 17:40:03 (consistent with Matlab CharFunTool v1.3.0, 24-Jun-2017 10:07:43).

References

[1] PHILLIPS, P.C.B. The true characteristic function of the F distribution. Biometrika (1982), 261-264.

[2] WITKOVSKY, V.: On the exact computation of the density and of the quantiles of linear combinations of t and F random variables. Journal of Statistical Planning and Inference 94 (2001), 1-13.

See Also

For more details see WIKIPEDIA: https://en.wikipedia.org/wiki/F-distribution.

Other Continuous Probability Distribution: cfS_Arcsine(), cfS_Beta(), cfS_Gaussian(), cfS_Laplace(), cfS_Rectangular(), cfS_Student(), cfS_TSP(), cfS_Trapezoidal(), cfS_Triangular(), cfS_Wigner(), cfX_ChiSquare(), cfX_Exponential(), cfX_Gamma(), cfX_InverseGamma(), cfX_LogNormal(), cf_ArcsineSymmetric(), cf_BetaNC(), cf_BetaSymmetric(), cf_Beta(), cf_ChiSquare(), cf_Exponential(), cf_FisherSnedecorNC(), cf_FisherSnedecor(), cf_Gamma(), cf_InverseGamma(), cf_Laplace(), cf_LogRV_BetaNC(), cf_LogRV_Beta(), cf_LogRV_ChiSquareNC(), cf_LogRV_ChiSquare(), cf_LogRV_FisherSnedecorNC(), cf_LogRV_FisherSnedecor(), cf_LogRV_MeansRatioW(), cf_LogRV_MeansRatio(), cf_LogRV_WilksLambdaNC(), cf_LogRV_WilksLambda(), cf_Normal(), cf_RectangularSymmetric(), cf_Student(), cf_TSPSymmetric(), cf_TrapezoidalSymmetric(), cf_TriangularSymmetric(), cf_vonMises()

Examples

## EXAMPLE 1
# CF of the F-distribution with df1 = 3, df2 = 5
df1 <- 3
df2 <- 5
t <- seq(-30, 30, length.out = 2^10+1)
plotReIm(function(t) cfX_FisherSnedecor(t, df1, df2), t,
         title = 'Characteristic function of the F-distribution')

## EXAMPLE 2
# PDF/CDF of the F-distribution with df1 = 3, df2 = 5
df1 <- 3
df2 <- 5
x <- seq(0, 25, length.out = 101)
prob <- c(0.9, 0.95, 0.99)
cf <- function(t) cfX_FisherSnedecor(t, df1, df2)
options <- list()
options$xMin <- 0
options$xMax <- 500
options$N <- 2^15
result <- cf2DistGP(cf, x, prob, options)

## EXAMPLE 3
# PDF/CDF of the compound Binomial-Fisher-Snedecor distribution
n <- 25
p <- 0.3
df1 <- 3
df2 <- 5
cfX <- function(t) cfX_FisherSnedecor(t, df1, df2)
cf <- function(t) cfN_Binomial(t, n, p, cfX)
x <- seq(0, 80, length.out = 101)
prob <- c(0.9, 0.95, 0.99)
options <- list()
options$isCompound <- TRUE
result <- cf2DistGP(cf, x, prob, options)
str(result)

gajdosandrej/CharFunToolR documentation built on June 3, 2024, 7:46 p.m.