
Density, distribution function, quantile function and random
generation for the log-gamma distribution with
location parameter location
,
scale parameter scale
and
shape parameter k
.
dlgamma(x, location = 0, scale = 1, shape = 1, log = FALSE)
plgamma(q, location = 0, scale = 1, shape = 1,
lower.tail = TRUE, log.p = FALSE)
qlgamma(p, location = 0, scale = 1, shape = 1,
lower.tail = TRUE, log.p = FALSE)
rlgamma(n, location = 0, scale = 1, shape = 1)
vector of quantiles.
vector of probabilities.
number of observations.
Same as runif
.
the location parameter
the (positive) scale parameter
the (positive) shape parameter
Logical.
If log = TRUE
then the logarithm of the density is returned.
dlgamma
gives the density,
plgamma
gives the distribution function,
qlgamma
gives the quantile function, and
rlgamma
generates random deviates.
See lgamma1
, the VGAM family function for
estimating the one parameter standard log-gamma distribution by maximum
likelihood estimation, for formulae and other details. Apart from
n
, all the above arguments may be vectors and are recyled to
the appropriate length if necessary.
Kotz, S. and Nadarajah, S. (2000). Extreme Value Distributions: Theory and Applications, pages 48--49, London: Imperial College Press.
# NOT RUN {
loc <- 1; Scale <- 1.5; shape <- 1.4
x <- seq(-3.2, 5, by = 0.01)
plot(x, dlgamma(x, loc = loc, Scale, shape = shape), type = "l",
col = "blue", ylim = 0:1,
main = "Blue is density, orange is cumulative distribution function",
sub = "Purple are 5,10,...,95 percentiles", las = 1, ylab = "")
abline(h = 0, col = "blue", lty = 2)
lines(qlgamma(seq(0.05, 0.95, by = 0.05), loc = loc, Scale, shape = shape),
dlgamma(qlgamma(seq(0.05, 0.95, by = 0.05), loc = loc, scale = Scale,
shape = shape),
loc = loc, Scale, shape = shape), col = "purple", lty = 3, type = "h")
lines(x, plgamma(x, loc = loc, Scale, shape = shape), col = "orange")
abline(h = 0, lty = 2)
# }
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