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Scan-Statistics-Project-4Y-INSA
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Add ScanStatMC to Experience plan

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Paul-Corbalan 2022-04-12 18:23:44 +02:00
parent 4d5bd81071
commit e5b5d717d9
1 changed files with 46 additions and 14 deletions
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60
Comparaison_of_methods.rmd
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@ -169,23 +169,22 @@ We compute the p-value associated to all 5 sequences, and stock them in a vector
```{r}
#We start by computing the empirical distribution for lambda0
Emp=EmpDistrib(lambda0,n_sample,T,tau)
pvalue=c()
index_scan=c()
Emp = EmpDistrib(lambda0,n_sample,T,tau)
scan = c()
pvalue = c()
index_scan = c()
#Then, we stock the p-value and the
for (i in 1:NbSeqH0){
ppi=DataH0[[i]]
result=PValue(Emp,DataH0[[i]],T,tau)
scan=c(scan,result[1])
pvalue=c(pvalue,result[2])
index_scan=c(index_scan,result[3])
#cat(paste("\nSimulation for the sequence", i, ", for lambda0=",lambda0, " ,lambda1=", lambda1, " , scan=", result[1] ,"p-value=",result[2]))
#print(length(ppi))
ppi = DataH0[[i]]
result = PValue(Emp,ppi,T,tau)
scan = c(scan,result[1])
pvalue = c(pvalue,result[2])
index_scan = c(index_scan,result[3])
}
#ScS_H0=data.frame(num=1:NbSeqH0, scan_stat=scan, pvalue_scan=pvalue, class=(pvalue<0.05), begin_scan=index_scan)
#sum(ScS_H0$class[which(ScS_H0$class==TRUE)])/NbSeqH0
ScS_H0=data.frame(num=(1:NbSeqH0), scan_stat=scan, pvalue_scan=pvalue,class=c(pvalue<0.05))
sum(ScS_H0$class[which(ScS_H0$class==TRUE)])/NbSeqH0
```
```{r}
@ -209,6 +208,25 @@ sum(ScS_H1$class[which(ScS_H0$class==TRUE)])/NbSeqH1
ScS_H1
```
```{r}
ScanStatMC <- function(NbSeq, T, tau, Emp, pp0){
scan=c()
pvalue=c()
index_scan=c()
for (i in 1:NbSeq){
ppi=pp0[[i]]
result=PValue(Emp,ppi,T,tau)
scan=c(scan,result[1])
pvalue=c(pvalue,result[2])
index_scan=c(index_scan,result[3])
}
ScS_H0=data.frame(num=(1:NbSeq), scan_stat=scan, pvalue_scan=pvalue,class=c(pvalue<0.05))
return(ScS_H0)
}
```
## 3. Local score
### Distribution of scores via Monte Carlo
```{r}
@ -294,7 +312,7 @@ LocaScoreMC <- function(lambda0, lambda1, NbSeq, T, X_seq, P_X, tbe0){
}
```
### Experience plan
## 4. Experience plan for comparaison
```{r}
NbSeq = 10**3
T = 10
@ -304,30 +322,44 @@ for (lambda0 in (2:5)){
cat("For T = ", T, ", Nb = ", NbSeq, "lambda0 = ", lambda0, "and lambda1 = ", lambda1, ":\n", sep = "")
tbe0=vector("list",length=NbSeq)
pp0 = vector("list", length = NbSeq)
for (i in (1:NbSeq)) {
ppi = PoissonProcess(lambda0,T)
ni=length(ppi)
pp0[[i]] = ppi
tbei=ppi[2:ni]-ppi[1:ni-1]
tbe0[[i]]=tbei
}
cat("- Empiric version:\n")
Score = ScoreDistribEmpiric(lambda0, lambda1, NbSeq, T)
Emp = EmpDistrib(lambda0,n_sample,T,tau)
X_seq = Score$Score_X
P_X = Score$P_X
LS_H0 = LocaScoreMC(lambda0, lambda1, NbSeq, T, X_seq, P_X, tbe0)
SS_H0 = ScanStatMC(NbSeq, T, tau, Emp, pp0)
cat("Local Score:\n")
print(summary(LS_H0))
cat("Scan Statistics:\n")
print(summary(SS_H0))
cat("- Elisa version:\n")
Score = ScoreDistribElisa(lambda0, lambda1, T)
Emp = EmpDistrib(lambda0,n_sample,T,tau)
X_seq = Score$Score_X
P_X = Score$P_X
LS_H0 = LocaScoreMC(lambda0, lambda1, NbSeq, T, X_seq, P_X, tbe0)
SS_H0 = ScanStatMC(NbSeq, T, tau, Emp, pp0)
cat("Local Score:\n")
print(summary(LS_H0))
cat("Scan Statistics:\n")
print(summary(SS_H0))
cat("---\n")
}
}