sght {fBasics} | R Documentation |
Standardized generalized hyperbolic Student-t Distribution
Description
Density, distribution function, quantile function and random generation for the standardized generalized hyperbolic Student-t distribution.
Usage
dsght(x, beta = 0.1, delta = 1, mu = 0, nu = 10, log = FALSE)
psght(q, beta = 0.1, delta = 1, mu = 0, nu = 10)
qsght(p, beta = 0.1, delta = 1, mu = 0, nu = 10)
rsght(n, beta = 0.1, delta = 1, mu = 0, nu = 10)
Arguments
x , q |
a numeric vector of quantiles. |
p |
a numeric vector of probabilities. |
n |
number of observations. |
beta |
numeric value, |
delta |
numeric value, the scale parameter, must be zero or positive. |
mu |
numeric value, the location parameter, by default 0. |
nu |
a numeric value, the number of degrees of freedom. Note,
|
log |
a logical, if TRUE, probabilities |
Details
dsght
gives the density,
psght
gives the distribution function,
qsght
gives the quantile function, and
rsght
generates random deviates.
These are the parameters in the first parameterization.
Value
numeric vector
Author(s)
Diethelm Wuertz
Examples
## rsght -
set.seed(1953)
r = rsght(5000, beta = 0.1, delta = 1, mu = 0, nu = 10)
plot(r, type = "l", col = "steelblue",
main = "gh: zeta=1 rho=0.5 lambda=1")
## dsght -
# Plot empirical density and compare with true density:
hist(r, n = 50, probability = TRUE, border = "white", col = "steelblue")
x = seq(-5, 5, length = 501)
lines(x, dsght(x, beta = 0.1, delta = 1, mu = 0, nu = 10))
## psght -
# Plot df and compare with true df:
plot(sort(r), (1:5000/5000), main = "Probability", col = "steelblue")
lines(x, psght(x, beta = 0.1, delta = 1, mu = 0, nu = 10))
## qsght -
# Compute Quantiles:
round(qsght(psght(seq(-5, 5, 1), beta = 0.1, delta = 1, mu = 0, nu =10),
beta = 0.1, delta = 1, mu = 0, nu = 10), 4)