Plotting the univariate generalised lambda distribution fits on the data set.
This function is designed for univariate generalised lambda distribution fits only.
fun.plot.fit(fit.obj, data, nclass = 50, xlab = "", name = "", param.vec, ylab="Density", main="")
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 |
Naming the type of distribution fits. |
param.vec |
A vector describing the type of generalised lambda
distribution used in the |
ylab |
Label on the y axis. |
main |
Title of the graph. |
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
## Generate Normally distribute random numbers as dataset
# junk<-rnorm(1000,3,2)
## Fit the data set using fun.data.fit.ml.
## Also, fun.data.fit.hs or fun.data.fit.hs.nw can be used.
# obj.fit<-fun.data.fit.ml(junk)
## Plot the resulting fits
# fun.plot.fit(obj.fit,junk,xlab="x",name=".ML",param.vec=c("rs","fmkl","fmkl"))
## This function also works for singular fits such as those by fun.RPRS.ml,
## fun.RMFMKL.ml, fun.RPRS.hs, fun.RMFMKL.hs, fun.RPRS.hs.nw, fun.RMFMKL.hs.nw
# junk<-rnorm(1000,3,2)
# obj.fit<-fun.RPRS.ml(junk)
# fun.plot.fit(obj.fit,junk,xlab="x",name="RPRS.ML",param.vec=c("rs"))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.