Do a quantile plot on the univariate distribution fits.
This plots the theoretical and actual data quantiles to allow the user to examine the adequacy of a single gld distribution fit.
qqplot.gld(data, fit, param, len = 10000, name = "", corner = "topleft",type="",range=c(0,1),xlab="",main="")
data |
Data fitted. |
fit |
Parameters of distribution fit. |
param |
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) ## Fit the function using fun.data.fit.ml # obj.fit1.ml<-fun.data.fit.ml(junk) ## Do a quantile plot on the raw quantiles # qqplot.gld(junk,obj.fit1.ml[,1],param="rs",name="RS ML fit") ## Or a qq plot to examine deviation from straight line # qqplot.gld(junk,obj.fit1.ml[,1],param="rs",name="RS ML fit",type="str.qqplot")
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.