View source: R/cfX_FisherSnedecor.R
| cfX_FisherSnedecor | R Documentation |
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.
cfX_FisherSnedecor(t, df1, df2, coef, niid, tol)
t |
vector or array of real values, where the CF is evaluated. |
df1 |
vector of the degrees of freedom |
df2 |
vector of the degrees of freedom |
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 |
niid |
scalar convolution coeficient |
tol |
tolerance factor for selecting the Poisson weights, i.e. such that |
Characteristic function cf(t) of the central FISHER-SNEDECOR F-distribution.
Ver.: 06-Oct-2018 17:40:03 (consistent with Matlab CharFunTool v1.3.0, 24-Jun-2017 10:07:43).
[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.
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()
## 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)
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