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Density, distribution function, quantile function and random generation for the generalized Rayleigh distribution.
dgenray(x, scale = 1, shape, log = FALSE)
pgenray(q, scale = 1, shape, lower.tail = TRUE, log.p = FALSE)
qgenray(p, scale = 1, shape, lower.tail = TRUE, log.p = FALSE)
rgenray(n, scale = 1, shape)
vector of quantiles.
vector of probabilities.
number of observations.
If length(n) > 1
then the length is taken to be the number required.
positive scale and shape parameters.
Logical.
If log = TRUE
then the logarithm of the density is returned.
dgenray
gives the density,
pgenray
gives the distribution function,
qgenray
gives the quantile function, and
rgenray
generates random deviates.
See genrayleigh
, the VGAM family function
for estimating the parameters,
for the formula of the probability density function and other details.
# NOT RUN {
shape <- 0.5; Scale <- 1; nn <- 501
x <- seq(-0.10, 3.0, len = nn)
plot(x, dgenray(x, shape, scale = Scale), type = "l", las = 1, ylim = c(0, 1.2),
ylab = paste("[dp]genray(shape = ", shape, ", scale = ", Scale, ")"),
col = "blue", cex.main = 0.8,
main = "Blue is density, orange is cumulative distribution function",
sub = "Purple lines are the 10,20,...,90 percentiles")
lines(x, pgenray(x, shape, scale = Scale), col = "orange")
probs <- seq(0.1, 0.9, by = 0.1)
Q <- qgenray(probs, shape, scale = Scale)
lines(Q, dgenray(Q, shape, scale = Scale), col = "purple", lty = 3, type = "h")
lines(Q, pgenray(Q, shape, scale = Scale), col = "purple", lty = 3, type = "h")
abline(h = probs, col = "purple", lty = 3)
max(abs(pgenray(Q, shape, scale = Scale) - probs)) # Should be 0
# }
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