CpBeeCell: Basic description

In Meiqian-Chen/GraphCpClust: This package is used to implement the change point estimation method and graph-based clustering method introduced in the three papers, and provide related data analysis.

Description

Given the true starting point, number of points, and the shortest Hamiltonian path, this function returns the change-point estimation based on the ratio cut.

Usage

CpBeeCell(ni,n1,n2,re.path)

Arguments

ni	true starting point
n1	always 1
n2	number of points
re.path	the shortest Hamiltonian path returned from the function Hpath

Value

the change-point estimate based on the ratio cut

Examples

data(Data_cell,Data_bee_A1,Data_bee_B1,package="GraphCpClust")
# Example in Shi, Wu and Rao (2017)
Lnorm_cell = function(x) sqrt(sum(t(x)*x))# Euclidean distance, but reader can define different distance such as max(abs(x)) and sum(abs(x)) 
Transf_cell = function(x) sqrt(x) #sqrt(x) log(1+x) # for cell images, squareroot transformation or log transformation

# Note: Have the data in an image folder in your working directory and in the .png package subfolder img. 
# for whole sequence of data 
data.trans=Distance.data_cell_bee(Data_cell,1,285,Lnorm_cell)
HD=data.trans$hd
SHP=CpBeeCell(1,1,285,Hpath_cell_bee(1,285,HD))

# cp1 = first change-point estimate 
# cp1 should be equal to SHP$Ratio.cut which is ratio cut change-point estiamte
cp1=SHP$Ratio.cp

# subsequence from from first node to cp1 (the first change-point estiamte)
# repeat procdure to the change-point estimate
data.trans=Distance.data_cell_bee(Data_cell,1,cp1,Lnorm_cell)
HD=data.trans$hd
SHP=CpBeeCell(1,1,cp1,Hpath_cell_bee(1,cp1,HD))

SHP$Sn # statistic - value shows whether our change-point test is signficant or not based on a comparison to the critical value from a table.
cp2=SHP$Ratio.cp

# subsequence fro cp1+1 to last node
# repeat procdure to the change-point estimate
data.trans=Distance.data_cell_bee(Data_cell,cp1+1,285,Lnorm_cell)
HD=data.trans$hd
SHP=CpBeeCell(cp1+1,1,285-cp1,Hpath_cell_bee(1,285-cp1,HD)) # first number is the true initial starting point location after the first change-point estimate

cp3=SHP$Ratio.cp
# input data must be a data list
data.trans=Distance.data_cell_bee(Data_cell,1,285,Lnorm_cell)
HD=data.trans$hd
SHP=CpBeeCell(1,1,285,Hpath_cell_bee(1,285,HD))
print(cp1)
print(cp2)
print(cp3)
# Example in Shi, Wu and Rao (2018)
#####################################################start
# fig4A
Lnorm_bee = function(x) abs(sum(x))  #sqrt(sum(t(x)*x))# sqrt(sum(t(x)*x))#sqrt(sum(t(x)*x)) max(abs(x)) #sum(abs(x))
Transf_bee = function(x) sqrt(x) #sqrt(x) log(1+x)

nb=1;ne=49;
nl=ne-nb+1
theta=0#thresheld
data.trans=Distance.data_cell_bee(Data_bee_A1,1,ne,Lnorm_bee)
HD=data.trans$hd
SHP1=CpBeeCell(1,1,ne,Hpath_cell_bee(1,ne,HD))

par(mar=c(4,5,1,1)+0.1,fig=c(0,1,0,1))
plot(x=c(1:(ne-1)),y=SHP1$Ratio.cut,ylab=expression(italic(C[t]^{'SHP(w*)'}/(t(N-t)))),xlab=expression(italic(t)), type="b", lwd=1.5,lty=3, pch=20) 
abline(v=which.min(SHP1$Ratio.cut[1:20]),lty=5)
abline(v=which.min(SHP1$Ratio.cut[20:ne])+19,lty=5)
#####################################################end

#####################################################start
# fig4B
Lnorm_bee = function(x) abs(sum(x))  #sqrt(sum(t(x)*x))# sqrt(sum(t(x)*x))#sqrt(sum(t(x)*x)) max(abs(x)) #sum(abs(x))
Transf_bee = function(x) sqrt(x) #sqrt(x) log(1+x)
nb=5;ne=49;
nl=ne-nb+1
theta=0#thresheld

data.trans=Distance.data_cell_bee(Data_bee_B1,1,45,Lnorm_bee)
HD=data.trans$hd
SHP2=CpBeeCell(1,1,45,Hpath_cell_bee(1,45,HD)) 

par(mar=c(4,5,1,1)+0.1,fig=c(0,1,0,1) )
plot(x=c(1:(45-1)),y=SHP2$Ratio.cut,ylab=expression(italic(C[t]^{'SHP(w*)'}/(t(N-t)))),xlab=expression(italic(t)), type="b", lwd=1.5,lty=3, pch=20) 
abline(v=which.min(SHP2$Ratio.cut[1:45]),lty=5)

Meiqian-Chen/GraphCpClust documentation built on April 27, 2021, 10:40 a.m.