Gld {Davies} | R Documentation |
Density, distribution function, quantile function and random generation for the Generalized Lambda Distribution
dgld(x, params) dgld.p(x, params) pgld(x, params) qgld(p, params) rgld(n, params)
x |
vector of quantiles. |
p |
vector of probabilities. |
n |
number of observations. If length(n)> 1 , the
length is taken to be the number required |
params |
vector of parameters: params[1]==lambda1 etseq |
The Generalized Lambda distribution has quantile function
f(x)=lambda1 +(p^lambda3 - (1-p)^lambda_4)/lambda_2
dgld
gives the density,
dgld.p
gives the density in terms of the quantile,
pgld
gives the distribution function,
qgld
gives the quantile function, and
rgld
generates random deviates.
Wichura, M. J. (1988) Algorithm AS 241: The Percentage Points of the Normal Distribution. Applied Statistics, 37, 477–484.
A. "{O}zt"{u}rk and R. F. Dale, "Least squares estimation of the parameters of the generalized lambda distribution", Technometrics 1985, 27(1):84
params <- c(4.114,0.1333,0.0193,0.1588) #taken straight from some paper gld.rv <- rgld(100,params) hist(gld.rv) fit.davies.q(gld.rv) #remember the Davies distn has 3 DF and the GLD 4...