Density, distribution function, quantile function and random generation for the exponential geometric distribution.
dexpgeom(x, scale = 1, shape, log = FALSE)
pexpgeom(q, scale = 1, shape)
qexpgeom(p, scale = 1, shape)
rexpgeom(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.
dexpgeom
gives the density,
pexpgeom
gives the distribution function,
qexpgeom
gives the quantile function, and
rexpgeom
generates random deviates.
See expgeometric
, 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, dexpgeom(x, scale, shape), type = "l", las = 1, ylim = c(0, 2),
ylab = paste("[dp]expgeom(shape = ", shape, ", scale = ", scale, ")"),
col = "blue", cex.main = 0.8,
main = "Blue is density, red is cumulative distribution function",
sub = "Purple lines are the 10,20,...,90 percentiles")
lines(x, pexpgeom(x, scale, shape), col = "red")
probs <- seq(0.1, 0.9, by = 0.1)
Q <- qexpgeom(probs, scale, shape)
lines(Q, dexpgeom(Q, scale, shape), col = "purple", lty = 3, type = "h")
lines(Q, pexpgeom(Q, scale, shape), col = "purple", lty = 3, type = "h")
abline(h = probs, col = "purple", lty = 3)
max(abs(pexpgeom(Q, scale, shape) - probs)) # Should be 0
# }
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