View source: R/cf_TrapezoidalSymmetric.R
| cf_TrapezoidalSymmetric | R Documentation | 
cf_TrapezoidalSymmetric(t, lambda, coef, niid) evaluates
the characteristic function of a linear combination (resp. convolution)
of independent zero-mean symmetric TRAPEZOIDAL random variables defined on the interval (-1,1).
That is, cf_TrapezoidalSymmetric evaluates the characteristic function
cf(t) of  Y = sum_{i=1}^N coef_i * X_i, where X_i ~ TrapezoidalSymmetric(\lambda_i)
are independent RVs defined on (-1,1), for all i = 1,...,N.
The characteristic function of X ~ TrapezoidalSymmetric(\lambda), with
E(X) = 0 and Var(X) = (1+\lambda^2)/6, is
cf(t) = cf_TrapezoidalSymmetric(t) = cf_RectangularSymmetric(w*t))*cf_RectangularSymmetric((1-w)*t) = (sin(w*t)/(w*t))*(sin((1-w)*t)/((1-w)*t)),
where w  = (1+\lambda)/2.
cf_TrapezoidalSymmetric(t, lambda, coef, niid)
| t | vector or array of real values, where the CF is evaluated. | 
| lambda | parameter of the offset,  | 
| coef | vector of the coefficients of the linear combination
of the zero-mean symmetric TRAPEZOIDAL random variables. If coef is scalar,
it is assumed that all coefficients are equal. If empty, default value is  | 
| niid | scalar convolution coeficient  | 
Characteristic function cf(t) of a linear combination
of independent zero-mean symmetric TRAPEZOIDAL random variables.
Ver.: 16-Sep-2018 18:39:29 (consistent with Matlab CharFunTool v1.3.0, 02-Jun-2017 12:08:24).
WITKOVSKY V. (2016). Numerical inversion of a characteristic function: An alternative tool to form the probability distribution of output quantity in linear measurement models. Acta IMEKO, 5(3), 32-44.
For more details see WIKIPEDIA: https://en.wikipedia.org/wiki/Trapezoidal_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_FisherSnedecor(),
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_TriangularSymmetric(),
cf_vonMises()
Other Symmetric Probability Distribution: 
cfS_Arcsine(),
cfS_Beta(),
cfS_Gaussian(),
cfS_Laplace(),
cfS_Rectangular(),
cfS_Student(),
cfS_Trapezoidal(),
cf_ArcsineSymmetric(),
cf_BetaSymmetric(),
cf_RectangularSymmetric(),
cf_TSPSymmetric()
## EXAMPLE 1
#CF of the symmetric Trapezoidal distribution with lambda = 1/2
lambda <- 1 / 2
t <- seq(from = -50,
         to = 50,
         length.out = 201)
plotReIm(function(t)
        cf_TrapezoidalSymmetric(t, lambda), t,
        title = "CF of the symmetric Trapezoidal distribution on (-1,1)")
## EXAMPLE 2
# CF of a linear combination of independent Trapezoidal RVs
t <- seq(from = -20,
         to = 20,
         length.out = 201)
lambda <- c(3, 3, 4, 4, 5) / 7
coef <- c(1, 2, 3, 4, 5) / 15
plotReIm(function(t)
        cf_TrapezoidalSymmetric(t, lambda, coef), t,
        title = "CF of a linear combination of independent Trapezoidal RVs")
## EXAMPLE 3
# PDF/CDF of a weighted linear combination of independent Trapezoidal RVs
lambda <- c(3, 3, 4, 4, 5) / 7
coef <- c(1, 2, 3, 4, 5) / 15
cf <- function(t)
        cf_TrapezoidalSymmetric(t, lambda, coef)
x <- seq(from = -1,
         to = 1,
         length.out = 201)
prob <- c(0.9, 0.95, 0.99)
options <- list()
options$N <- 2 ^ 12
options$xMin <- -1
options$xMax <- 1
result <- cf2DistGP(cf, x, prob, options)
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