Description Usage Arguments Details Value Author(s) Examples
Density, distribution function, quantile function and random generation for the discrete gamma-GPD spliced threshold distribution. The distribution has gamma bulk with shape equal to shape
and rate equal to rate
. It is spliced at a threshold equal to u
and has a GPD tail with sigma equal to sigma
and xi equal to xi
. The proportion of data above the threshold phi is equal to phiu
and the data are shifted according to shift
.
1 2 3 4 | ddiscgammagpd(x, fit, shape, rate, u, sigma, xi, phiu = NULL, shift = 0, log = FALSE)
pdiscgammagpd(q, fit, shape, rate, u, sigma, xi, phiu = NULL, shift = 0)
qdiscgammagpd(p, fit, shape, rate, u, sigma, xi, phiu = NULL, shift = 0)
rdiscgammagpd(n, fit, shape, rate, u, sigma, xi, phiu = NULL, shift = 0)
|
x, q |
vector of quantiles. |
p |
vector of probabilities. |
n |
number of observations. |
fit |
A fit output from fdiscgammagpd. If this object is passed, all parameter fields will automatically populate with the maximum likelihood estimates for the parameters in fit. |
shape |
shape parameter alpha of the truncated gamma distribution. |
rate |
rate parameter beta of the gamma distribution. |
u |
threshold. |
sigma |
scale parameter sigma of the GPD. |
xi |
shape parameter xi of the GPD |
phiu |
Propotion of data greater than or equal to threshold u. |
shift |
value the complete distribution is shifted by. Ideally, this is the smallest value of the count data from one sample. |
log |
Logical; if TRUE, probabilities p are given as log(p). |
The shape, rate, u, sigma, and xi parameters must be specified by the user. If phiu
is left unspecified, it defaults to 1 minus the distribution function of a discrete gamma distribution (not a discrete truncated gamma) evaluated at u-1
.
ddiscgammagpd
gives the density, pdiscgammagpd
gives the distribution function, qdiscgammagpd
gives the quantile function, and rdiscgammagpd
generates random variables from the described distribution.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | # Generate and sort a random sample for a log-log plot
d <- rdiscgammagpd(100, shape = 5, rate = .25, u = 25,
sigma = 15, xi = .5, shift = 1)
d <- sort(d, decreasing = TRUE)
plot(log(d), log(1:100))
# When phiu is specified to .2, exactly 80%
# of the data are below the threshold u
pdiscgammagpd(24, shape = 5, rate = .25, u = 25,
sigma = 15, xi = .5, phiu = .2, shift = 1)
# Plot simulated data versus theoretical quantiles
quantiles <- qdiscgammagpd((100:1)/101, shape = 5, rate = .25, u = 25,
sigma = 15, xi = .5, shift = 1)
plot(log(d), log(quantiles))
abline(0,1) # The line x=y
# Density below shift value should be 0
ddiscgammagpd(0, shape = 5, rate = .25, u = 25, sigma = 15, xi = .5, shift = 1)
# This is an example of using the "fit" input instead of manually specifying all parameters
data("repertoires")
thresholds1 <- unique(round(quantile(repertoires[[1]], c(.75,.8,.85,.9,.95))))
fit1 <- fdiscgammagpd(repertoires[[1]], useq = thresholds1, shift = min(repertoires[[1]]))
qdiscgammagpd(.6, fit1)
|
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