| sght {fBasics} | R Documentation |
Density, distribution function, quantile function and random generation for the standardized generalized hyperbolic distribution.
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)
beta, delta, mu |
numeric values.
|
nu |
a numeric value, the number of degrees of freedom.
Note, |
x, q |
a numeric vector of quantiles. |
p |
a numeric vector of probabilities. |
n |
number of observations. |
log |
a logical, if TRUE, probabilities |
All values for the *sght functions are numeric vectors:
d* returns the density,
p* returns the distribution function,
q* returns the quantile function, and
r* generates random deviates.
All values have attributes named "param" listing
the values of the distributional parameters.
Diethelm Wuertz.
## 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)