dhalflogistic | R Documentation |
Half-logistic density, distribution, and quantile functions, random number generation and expectation and variance.
dhalflogistic(x, scale=1, log=FALSE)
phalflogistic(q, scale=1)
qhalflogistic(p, scale=1)
rhalflogistic(n, scale=1)
ehalflogistic(scale=1)
vhalflogistic(scale=1)
x, q |
quantile. |
p |
probability. |
n |
number of observations. |
scale |
scale parameter ( |
log |
logical; if |
The half-logistic distribution is simply a zero-mean logistic distribution
that is restricted to take only positive values.
If X\sim\mathrm{logistic}
, then
|sX|\sim\mathrm{halflogistic}(\mathrm{scale}\!=\!s)
.
‘dhalflogistic()
’ gives the density function,
‘phalflogistic()
’ gives the cumulative distribution
function (CDF),
‘qhalflogistic()
’ gives the quantile function (inverse CDF),
and ‘rhalflogistic()
’ generates random deviates.
The ‘ehalflogistic()
’ and ‘vhalflogistic()
’
functions return the corresponding half-logistic distribution's
expectation and variance, respectively.
Christian Roever christian.roever@med.uni-goettingen.de
C. Roever, R. Bender, S. Dias, C.H. Schmid, H. Schmidli, S. Sturtz, S. Weber, T. Friede. On weakly informative prior distributions for the heterogeneity parameter in Bayesian random-effects meta-analysis. Research Synthesis Methods, 12(4):448-474, 2021. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/jrsm.1475")}.
N.L. Johnson, S. Kotz, N. Balakrishnan. Continuous univariate distributions, volume 2, chapter 23.11. Wiley, New York, 2nd edition, 1994.
dlogis
, dhalfnormal
,
dlomax
, drayleigh
,
TurnerEtAlPrior
, RhodesEtAlPrior
,
bayesmeta
.
#######################
# illustrate densities:
x <- seq(0,6,le=200)
plot(x, dhalfnormal(x), type="l", col="red", ylim=c(0,1),
xlab=expression(tau), ylab=expression("probability density "*f(tau)))
lines(x, dhalflogistic(x), col="green3")
lines(x, dhalfcauchy(x), col="blue")
lines(x, dexp(x), col="cyan")
abline(h=0, v=0, col="grey")
# show log-densities (note the differing tail behaviour):
plot(x, dhalfnormal(x), type="l", col="red", ylim=c(0.001,1), log="y",
xlab=expression(tau), ylab=expression("probability density "*f(tau)))
lines(x, dhalflogistic(x), col="green3")
lines(x, dhalfcauchy(x), col="blue")
lines(x, dexp(x), col="cyan")
abline(v=0, col="grey")
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