dhgld: The Hurdle Generalized Lambda Distribution Family
In dmarcondes/HGLD: Fit the Hurdle Generalized Lambda Distribution

Description Usage Arguments Details Value References Examples

Density of the Hurdle Generalized Lambda Distribution.

1 2	dhgld(x, mixture = FALSE, lambda1, lambda2 = NULL, prob = NULL, param = "fmkl", inverse.eps = 1e-08, max.iterations = 500)

`x`	Vector of data.
`mixture`	Whether to give the density of a mixture of HGLDs.
`lambda1`	A vector of length 5 with the five parameters of the HGLD, or of the first HGLD if mixture = TRUE.
`lambda2`	A vector of length 4 with the four parameters of the second HGLD if mixture = TRUE.
`prob`	The cluster parameter (probability) for the mixture of HGLDs.
`param`	"fmkl" or "rs".
`inverse.eps`	Accuracy of calculation for the numerical determination of F(x), defaults to 1e-8.
`max.iterations`	Maximum number of iterations in the numerical determination of F(x), defaults to 500.

If the parametrization of the RS or FMKL HGLD is not acceptable, the function returns NA. This function is based on the GLDEX package.

The probability density of the continuous part of the HGLD.

Marcondes, D.; Peixoto, C.; Maia, A. C.; A Survey of a Hurdle Model for Heavy-Tailed Data Based on the Generalized Lambda Distribution. (2017) arxiv1712.02183

Su, S.; Fitting Single and Mixture of Generalized Lambda Distributions to Data via Discretized and Maximum Likelihood Methods: GLDEX in R. (2007), Journal of Statistical Software: *21* 9.

library(ggplot2)
{ggplot(data.frame(x = seq(-3,1,0.01)),aes(x = x)) +
stat_function(fun = function(x) dhgld(x = x,
                                     lambda1 = c(0.48,0.509,-0.000369,-0.0002483,-0,0003916),
                                     param = "rs"))}

#mixture
lambda1 <- c(0.230,0.3514,-0.4472,-0.374,-0.3108)
lambda2 <- c(9.624,-1.227,-0.629,-0.8515)
{ggplot(data.frame(x = seq(-10,20,0.25)),aes(x = x)) +
stat_function(fun = function(x) dhgld(x = x,mixture = TRUE,lambda1 = lambda1,
lambda2 = lambda2,prob = 0.47954,param = "rs"))}