Generalized Lambda Distribution
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.