qqplot.gld.bi {GLDEX} | 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
# 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")