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)