Landau Distribution | R Documentation |
Density, distribution function, quantile function and random number generation for the Landau distribution with location parameter mu and scale parameter sigma.
dLandau(x, mu=log(pi/2), sigma=pi/2, log=FALSE)
pLandau(x, mu=log(pi/2), sigma=pi/2, log=FALSE, lower.tail=TRUE)
qLandau(p, mu=log(pi/2), sigma=pi/2, log=FALSE, lower.tail=TRUE)
rLandau(n, mu=log(pi/2), sigma=pi/2)
x |
The value or vector of values of the Landau-distributed random variable. |
mu |
The location parameter of the Landau distribution. Defaults to log(pi/2) to give Landau's original distribution. |
sigma |
The scale parameter of the Landau distribution. Defaults to pi/2 to give Landau's original distribution. |
log |
If true the log probability is output. |
lower.tail |
If true (the default) the lower tail probability is returned. Otherwise the upper tail probability. |
p |
The value or vector of values, between 0 and 1, of the probability specifying the quantile for which to return the Landau random variable |
n |
The number of values to simulate. |
The density of the Landau distribution can be written
f(x)=\frac{1}{\pi\,\sigma}\int_0^\infty \exp\left(-t\frac{(x-\mu)}{\sigma}-\frac{2}{\pi}t\log(t)\right)\,\sin\left(2t\right)\textrm{d}t
dLandau
produces the density, pLandau
the tail probability, qLandau
the quantile and rLandau
random variates for the Landau distribution.
Daniel J. Wilson
Landau LD (1944) On the energy loss of fast particles by ionization. J Phys USSR 8:201-205.
Daniel J. Wilson (2019) The harmonic mean p-value for combining dependent tests. Proceedings of the National Academy of Sciences USA 116: 1195-1200.
p.hmp
# For detailed examples type vignette("harmonicmeanp")
# Example: simulate from a non-uniform distribution mildly enriched for small \emph{p}-values.
# Compare the significance of the combined p-value for Bonferroni, Benjamini-Hochberg (i.e. Simes),
# HMP and (equivalently) MAMML with 2 degrees of freedom.
L = 1000
p = rbeta(L,1/1.5,1)
min(p.adjust(p,"bonferroni"))
min(p.adjust(p,"BH"))
x = hmp.stat(p)
pLandau(1/x,log(length(p))+(1 + digamma(1) - log(2/pi)),pi/2,lower.tail=FALSE)
p.hmp(p,L=L)
p.mamml(1/p,2,L=L)
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