Plotting mixture of two generalised lambda distributions on the data set.
This function is designed for mixture of two generalised lambda distributions only.
fun.plot.fit.bm(fit.obj, data, nclass = 50, xlab = "", name = "", main="", param.vec, ylab="Density")
fit.obj |
Fitted object from |
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 |
Title of the graph. |
param.vec |
A vector describing the type of generalised lambda
distribution used in the |
ylab |
Label on the y axis. |
A graphical output showing the data and the resulting distributional fits.
If the distribution fits over fits the peak of the distribution, it can be difficult to see the actual data set.
Steve Su
# 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"))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.