# Loading equivalent dose data
data(EDdata)
# Specifying the number of component to be 1 to do central age model analysis
obj<-RadialPlotter(EDdata$gl11,ncomp=1,zscale=seq(20,37,3))
obj
# Fitting finite mxiture age model with a number of components of 7 (k=7)
obj<-RadialPlotter(EDdata$al3,ncomp=7,zscale=seq(25,90,5),pcolor="gray")
print(obj)
# Picking out the appropriate number of components then fitting automatically
obj<-RadialPlotter(EDdata$al3,zscale=seq(24,93,7),samplename="AL3",pcolor="brown")
obj
# In a radical but not advisable case, one may set maxcomp to be the largest
# looping number of components that allowed
obj<-RadialPlotter(EDdata$gl11,maxcomp=35,samplename="GL11",zscale=seq(20,37,3))
unclass(obj)
# Setting zscale manually
zscale<-seq(min(EDdata$al3[,1])-1,max(EDdata$al3[,1])+1,by = 15L)
obj<-RadialPlotter(EDdata$al3,maxcomp=13,zscale=zscale,kratio=0.5)
print(obj)
# Performing minimum age model analysis with three parameters
obj<-RadialPlotter(EDdata$gl11,ncomp=-1,maxiter=100,zscale=seq(20,37,3))
class(obj)
# Performing minimum age model analysis with three parameters (addsigma=0.1)
print(RadialPlotter(EDdata$al3,ncomp=-1,addsigma=0.1,kratio=0.5,
maxiter=100,algorithm="port",zscale=seq(24,93,7)))
# Performing minimum age model analysis with four parameters
obj<-RadialPlotter(EDdata$al3,ncomp=-2,maxiter=100,zscale=seq(24,93,7))
unclass(obj)Run the code above in your browser using DataLab