GLDEX (version 2.0.0.9.3)

fun.plot.fit.bm: Plotting mixture of two generalised lambda distributions on the data set.

Description

This function is designed for mixture of two generalised lambda distributions only.

Usage

fun.plot.fit.bm(fit.obj, data, nclass = 50, xlab = "", name = "", main="", 
param.vec, ylab="Density")

Value

A graphical output showing the data and the resulting distributional fits.

Arguments

fit.obj

Fitted object from fun.auto.bimodal.ml, fun.auto.bimodal.pml

data

Dataset to be plotted.

nclass

Number of class of histogram, the default is 50.

xlab

Label on the x axis.

name

Legend, usually used to identify type of GLD used if main is provided. If main is not provided, then this is used in the title.

main

Title of the graph.

param.vec

A vector describing the type of generalised lambda distribution used in the fit.obj.

ylab

Label on the y axis.

Author

Steve Su

See Also

fun.auto.bimodal.ml, fun.auto.bimodal.pml, fun.plot.fit

Examples

# \donttest{

 opar <- par() 
 par(mfrow=c(2,1))

# Fitting mixture of generalised lambda distributions on the data set using 
# both the maximum likelihood and partition maximum likelihood and plot 
# the resulting fits

 junk<-fun.auto.bimodal.ml(faithful[,1],per.of.mix=0.1,clustering.m=clara,
 init1.sel="rprs",init2.sel="rmfmkl",init1=c(-1.5,1,5),init2=c(-0.25,1.5),
 leap1=3,leap2=3)
 fun.plot.fit.bm(nclass=50,fit.obj=junk,data=faithful[,1],
 name="Maximum likelihood using",xlab="faithful1",param.vec=c("rs","fmkl"))

 junk<-fun.auto.bimodal.pml(faithful[,1],clustering.m=clara,init1.sel="rprs",
 init2.sel="rmfmkl",init1=c(-1.5,1,5),init2=c(-0.25,1.5),leap1=3,leap2=3)
 fun.plot.fit.bm(nclass=50,fit.obj=junk,data=faithful[,1],
 name="Partition maximum likelihood using",xlab="faithful1",
 param.vec=c("rs","fmkl"))

 junk<-fun.auto.bimodal.ml(faithful[,1],per.of.mix=0.1,clustering.m=clara,
 init1.sel="rprs",init2.sel="rmfmkl",init1=c(-1.5,1,5),init2=c(-0.25,1.5),
 leap1=3,leap2=3)
 fun.plot.fit.bm(nclass=50,fit.obj=junk,data=faithful[,1],
 main="Mixture distribution fit",
 name="RS and FMKL GLD",xlab="faithful1",param.vec=c("rs","fmkl"))

 par(opar)
# }