pdfemu | R Documentation |
This function computes the probability density of the Eta-Mu (\eta:\mu
) distribution given parameters (\eta
and \mu
) computed by paremu
. The probability density function is
f(x) = \frac{4\sqrt{\pi}\mu^{\mu - 1/2}h^\mu}{\gamma(\mu)H^{\mu - 1/2}}\,x^{2\mu}\,\exp(-2\mu h x^2)\,I_{\mu-1/2}(2\mu H x^2)\mbox{,}
where f(x)
is the nonexceedance probability for quantile x
, and the modified Bessel function of the first kind is I_k(x)
, and the h
and H
are
h = \frac{1}{1-\eta^2}\mbox{,}
and
H = \frac{\eta}{1-\eta^2}\mbox{,}
for “Format 2” as described by Yacoub (2007). This format is exclusively used in the algorithms of the lmomco package.
If \mu=1
, then the Rice distribution results, although pdfrice
is not used. If \kappa \rightarrow 0
, then the exact Nakagami-m density function results with a close relation to the Rayleigh distribution.
Define m
as
m = 2\mu\biggl[1 + {\biggr(\frac{H}{h}\biggl)}^2 \biggr]\mbox{,}
where for a given m
, the parameter \mu
must lie in the range
m/2 \le \mu \le m\mbox{.}
The I_k(x)
for real x > 0
and noninteger k
is
I_k(x) = \frac{1}{\pi} \int_0^\pi \exp(x\cos(\theta)) \cos(k \theta)\; \mathrm{d}\theta - \frac{\sin(k\pi)}{\pi}\int_0^\infty \exp(-x \mathrm{cosh}(t) - kt)\; \mathrm{d}t\mbox{.}
pdfemu(x, para, paracheck=TRUE)
x |
A real value vector. |
para |
The parameters from |
paracheck |
A logical controlling whether the parameters and checked for validity. |
Probability density (f
) for x
.
W.H. Asquith
Yacoub, M.D., 2007, The kappa-mu distribution and the eta-mu distribution: IEEE Antennas and Propagation Magazine, v. 49, no. 1, pp. 68–81
cdfemu
, quaemu
, lmomemu
, paremu
## Not run:
x <- seq(0,4, by=.1)
para <- vec2par(c(.5, 1.4), type="emu")
F <- cdfemu(x, para); X <- quaemu(F, para)
plot(F, X, type="l", lwd=8); lines(F, x, col=2)
delx <- 0.005
x <- seq(0,3, by=delx)
plot(c(0,3), c(0,1), xaxs="i", yaxs="i",
xlab="RHO", ylab="pdfemu(RHO)", type="n")
mu <- 0.6
# Note that in order to produce the figure correctly using the etas
# shown in the figure that it must be recognized that these are the etas
# for format1, but all of the algorithms in lmomco are built around
# format2
etas.format1 <- c(0, 0.02, 0.05, 0.1, 0.2, 0.3, 0.5, 1)
etas.format2 <- (1 - etas.format1)/(1+etas.format1)
H <- etas.format2 / (1 - etas.format2^2)
h <- 1 / (1 - etas.format2^2)
for(eta in etas.format2) {
lines(x, pdfemu(x, vec2par(c(eta, mu), type="emu")),
col=rgb(eta^2,0,0))
}
mtext("Yacoub (2007, figure 5)")
plot(c(0,3), c(0,2), xaxs="i", yaxs="i",
xlab="RHO", ylab="pdfemu(RHO)", type="n")
eta.format1 <- 0.5
eta.format2 <- (1 - eta.format1)/(1 + eta.format1)
mus <- c(0.25, 0.3, 0.5, 0.75, 1, 1.5, 2, 3)
for(mu in mus) {
lines(x, pdfemu(x, vec2par(c(eta, mu), type="emu")))
}
mtext("Yacoub (2007, figure 6)")
plot(c(0,3), c(0,1), xaxs="i", yaxs="i",
xlab="RHO", ylab="pdfemu(RHO)", type="n")
m <- 0.75
mus <- c(0.7425, 0.75, 0.7125, 0.675, 0.45, 0.5, 0.6)
for(mu in mus) {
eta <- sqrt((m / (2*mu))^-1 - 1)
print(eta)
lines(x, pdfemu(x, vec2par(c(eta, mu), type="emu")))
}
mtext("Yacoub (2007, figure 7)") #
## End(Not run)
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