qqplot.gld.bi | R Documentation |
This plots the theoretical and actual data quantiles to allow the user to examine the adequacy of two gld distributions mixture fit.
qqplot.gld.bi(data, fit, param1, param2, len = 10000, name = "",
corner = "topleft",type="",range=c(0,1),xlab="",main="")
data |
Data fitted. |
fit |
Parameters of distribution fit. |
param1 |
Can be either |
param2 |
Can be either |
len |
Precision of the quantile calculatons. Default is 10000. This means 10000 points are taken from 0 to 1. |
name |
Name of the data set, added to the title of plot if |
corner |
Can be |
type |
This can be "" or "str.qqplot", the first produces the raw quantiles and the second plot them on a straight line. Default is "". |
range |
This is the range for which the quantiles are to be plotted.
Default is |
xlab |
x axis label, if left blank, then default is "Data" |
main |
Title of the plot, if left blank, a default title will be added. |
A plot is given.
Steve Su
qqplot.gld
set.seed(1000)
junk<-rweibull(300,3,2)
## 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"))
## Do a quantile plot on the raw quantiles
qqplot.gld.bi(faithful[,1],junk$par,param1="rs",param2="fmkl",
name="RS FMKL ML fit")
## Or a qq plot to examine deviation from straight line
qqplot.gld.bi(faithful[,1],junk$par,param1="rs",param2="fmkl",
name="RS FMKL ML fit",type="str.qqplot")
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