Calculates the theoretical mean, variance, skewness and kurtosis for mixture of two generalised lambda distributions.
This is the bimodal counterpart for fun.comp.moments.ml.2 and
fun.comp.moments.ml.
fun.theo.bi.mv.gld(L1, L2, L3, L4, param1, M1, M2, M3, M4, param2, p1, normalise="N")
L1 |
Location parameter of the first generalised lambda distribution. Or all the parameters of mixture distribution in the form of c(L1,L2,L3,L4,M1,M2,M3,M4,p), you still must specify param1 and param2. |
L2 |
Scale parameter of the first generalised lambda distribution. |
L3 |
First shape parameter of the first generalised lambda distribution. |
L4 |
Second shape parameter of the first generalised lambda distribution. |
param1 |
|
M1 |
Location parameter of the second generalised lambda distribution |
M2 |
Scale parameter of the second generalised lambda distribution. |
M3 |
First shape parameter of the second generalised lambda distribution. |
M4 |
Second shape parameter of the second generalised lambda distribution. |
param2 |
|
p1 |
Proportion of the first generalisd lambda distribution. |
normalise |
"Y" if you want kurtosis to be calculated with reference to kurtosis = 0 under Normal distribution. |
A vector showing the theoretical mean, variance, skewness and kurtosis for mixture of two generalised lambda distributions.
The theoretical moments may not always exist for generalised lambda distributions.
Steve Su
Freimer, M., Mudholkar, G. S., Kollia, G. & Lin, C. T. (1988), A study of the generalized tukey lambda family, Communications in Statistics - Theory and Methods *17*, 3547-3567.
Karian, Zaven A. and Dudewicz, Edward J. (2000), Fitting statistical distributions: the Generalized Lambda Distribution and Generalized Bootstrap methods, Chapman & Hall
Ramberg, J. S. & Schmeiser, B. W. (1974), An approximate method for generating asymmetric random variables, Communications of the ACM *17*, 78-82.
## Fits the Old Faithful geyser data (first column) using the maximum ## likelihood. # fit1<-fun.auto.bimodal.ml(faithful[,1],init1.sel="rmfmkl",init2.sel="rmfmkl", # init1=c(-0.25,1.5),init2=c(-0.25,1.5),leap1=3,leap2=3) ## Find the theoretical moments of the fit # fun.theo.bi.mv.gld(fit1$par[1],fit1$par[2],fit1$par[3],fit1$par[4],"fmkl", # fit1$par[5],fit1$par[6],fit1$par[7],fit1$par[8],"fmkl",fit1$par[9]) ## Compare this with the empirical moments from the data set. # fun.moments(faithful[,1])
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