gld {fBasics} | R Documentation |
Density, distribution function, quantile function and random generation for the generalized lambda distribution.
dgld(x, lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8, log = FALSE) pgld(q, lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8) qgld(p, lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8) rgld(n, lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8)
lambda1, lambda2, lambda3, lambda4 |
are numeric values where
|
n |
number of observations. |
p |
a numeric vector of probabilities. |
x, q |
a numeric vector of quantiles. |
log |
a logical, if TRUE, probabilities |
All values for the *gld
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.
Chong Gu for code implemented from R's
contributed package gld
.
## rgld - set.seed(1953) r = rgld(500, lambda1=0, lambda2=-1, lambda3=-1/8, lambda4=-1/8) plot(r, type = "l", col = "steelblue", main = "gld: lambda1=0 lambda2=-1 lambda3/4=-1/8") ## dgld - # Plot empirical density and compare with true density: hist(r, n = 25, probability = TRUE, border = "white", col = "steelblue") x = seq(-5, 5, 0.25) lines(x, dgld(x, lambda1=0, lambda2=-1, lambda3=-1/8, lambda4=-1/8)) ## pgld - # Plot df and compare with true df: plot(sort(r), ((1:500)-0.5)/500, main = "Probability", col = "steelblue") lines(x, pgld(x, lambda1=0, lambda2=-1, lambda3=-1/8, lambda4=-1/8)) ## qgld - # Compute Quantiles: qgld(pgld(seq(-5, 5, 1), lambda1=0, lambda2=-1, lambda3=-1/8, lambda4=-1/8), lambda1=0, lambda2=-1, lambda3=-1/8, lambda4=-1/8)