Update Comparaison_of_methods.rmd
Plot Sensitivity and Specificity for confusion matrix and correction of method for simulation under H1
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njulia1
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@ -30,7 +30,7 @@ SimulationH1 <- function(lambda0, lambda1,T,tau){
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ppH0_avant=ppH0bis[which(ppH0bis<ppH1.repo[1])]
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ppH0_avant=ppH0bis[which(ppH0bis<ppH1.repo[1])]
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ppH0_apres=ppH0bis[which(ppH0bis>ppH1.repo[length(ppH1.repo)])]
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ppH0_apres=ppH0bis[which(ppH0bis>ppH1.repo[length(ppH1.repo)])]
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ppH1=c(ppH0_avant,ppH1.repo,ppH0_apres)
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ppH1=c(ppH0_avant,ppH1.repo,ppH0_apres)
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return (c(ppH1,which(ppH1==min(ppH1.repo))))
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return (ppH1)
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}
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}
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```
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```
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@ -130,12 +130,12 @@ PValue <- function(Emp,ppH1, T, tau){
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### 2.2. Simulation under $\mathcal{H}_0$ and computation of p-values
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### 2.2. Simulation under $\mathcal{H}_0$ and computation of p-values
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On simule des séquences sous $\mathcal{H}_0$, que l'on stocke. On calcule la valeur de la scan stat et de la p-value, que l'on stocke aussi. On a une séquence de p-valeur des scans et une séquence de score local.
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On simule des séquences sous $\mathcal{H}_0$, que l'on stocke. On calcule la valeur de la scan stat et de la p-value, que l'on stocke aussi. On a une séquence de p-valeur des scans et une séquence de score local.
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```{r}
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```{r}
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NbSeqH0=1000
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NbSeqH0=10000
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NbSeqH1=NbSeqH0
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NbSeqH1=NbSeqH0
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DataH0=vector("list")
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DataH0=vector("list")
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DataH1=vector("list")
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DataH1=vector("list")
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lambda0=3
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lambda0=4
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lambda1=30
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lambda1=10
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T=10
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T=10
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tau=1
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tau=1
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@ -149,8 +149,7 @@ for (i in 1:NbSeqH0) {
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seqH1begin=c()
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seqH1begin=c()
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for (i in 1:NbSeqH1) {
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for (i in 1:NbSeqH1) {
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pphi=SimulationH1(lambda0, lambda1,T,tau)
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pphi=SimulationH1(lambda0, lambda1,T,tau)
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DataH1[[i]]=pphi[1]
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DataH1[[i]]=pphi
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seqH1begin=c(pphi[2],seqH1begin)
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}
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}
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#Computation of the time between events
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#Computation of the time between events
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@ -201,12 +200,10 @@ for (i in 1:NbSeqH1){
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scan=c(scan,result[1])
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scan=c(scan,result[1])
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pvalue=c(pvalue,result[2])
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pvalue=c(pvalue,result[2])
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index_scan=c(index_scan,result[3])
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index_scan=c(index_scan,result[3])
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#cat(paste("\nSimulation for the sequence", i, ", for lambda0=",lambda0, " ,lambda1=", lambda1, " , scan=", result[1] ,"p-value=",result[2]))
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#print(length(ppi))
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}
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}
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ScS_H1=data.frame(num=1:NbSeqH1, scan_stat=scan, pvalue_scan=pvalue, class=(pvalue<0.05), begin_scan=index_scan, begin_seq_H1=seqH1begin)
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ScS_H1=data.frame(num=1:NbSeqH1, scan_stat=scan, pvalue_scan=pvalue, class=(pvalue<0.05), begin_scan=index_scan)
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sum(ScS_H1$class[which(ScS_H0$class==TRUE)])/NbSeqH1
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sum(ScS_H1$class[which(ScS_H1$class==TRUE)])/NbSeqH1
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ScS_H1
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```
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```
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```{r}
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```{r}
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@ -339,55 +336,73 @@ LocalScoreMC <- function(lambda0, lambda1, NbSeq, T, X_seq, P_X, tbe0){
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NbSeq = 10**3
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NbSeq = 10**3
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T = 10
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T = 10
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for (lambda0 in (2:5)){
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for (lambda0 in (2:5)){
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for (lambda1 in c(2,4,6)){
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Sensitivity = c()
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if (lambda0 < lambda1){
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Specificity = c()
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cat("For T = ", T, ", Nb = ", NbSeq, "lambda0 = ", lambda0, "and lambda1 = ", lambda1, ":\n", sep = "")
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accepted_lambda = c()
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tbe0=vector("list",length=NbSeq)
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pp0 = vector("list", length = NbSeq)
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for (i in (1:NbSeq)) {
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ppi = PoissonProcess(lambda0,T)
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ni=length(ppi)
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pp0[[i]] = ppi
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tbei=ppi[2:ni]-ppi[1:ni-1]
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tbe0[[i]]=tbei
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}
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cat("- Empiric version:\n")
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Score = ScoreDistribEmpiric(lambda0, lambda1, NbSeq, T)
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Emp = EmpDistrib(lambda0,n_sample,T,tau)
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X_seq = Score$Score_X
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for (lambda1 in c(3:8)){
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P_X = Score$P_X
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if (lambda0 < lambda1){
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accepted_lambda=c(accepted_lambda,lambda1)
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LS_H0 = LocalScoreMC(lambda0, lambda1, NbSeq, T, X_seq, P_X, tbe0)
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cat("For T = ", T, ", Nb = ", NbSeq, ", lambda0 = ", lambda0, " and lambda1 = ", lambda1, ":\n", sep = "")
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SS_H0 = ScanStatMC(NbSeq, T, tau, Emp, pp0)
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tbe0=vector("list",length=NbSeq)
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pp0 = vector("list", length = NbSeq)
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cat("Local Score:\n")
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for (i in (1:NbSeq)) {
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print(summary(LS_H0))
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ppi = PoissonProcess(lambda0,T)
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cat("Scan Statistics:\n")
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ni=length(ppi)
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print(summary(SS_H0))
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pp0[[i]] = ppi
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cat("Confusion Matrix:\n")
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tbei=ppi[2:ni]-ppi[1:ni-1]
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print(confusionMatrix(factor(LS_H0$class), factor(SS_H0$class)))
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tbe0[[i]]=tbei
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cat("- Elisa version:\n")
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Score = ScoreDistribElisa(lambda0, lambda1, T)
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Emp = EmpDistrib(lambda0,n_sample,T,tau)
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X_seq = Score$Score_X
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P_X = Score$P_X
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LS_H0 = LocalScoreMC(lambda0, lambda1, NbSeq, T, X_seq, P_X, tbe0)
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SS_H0 = ScanStatMC(NbSeq, T, tau, Emp, pp0)
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cat("Local Score:\n")
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print(summary(LS_H0))
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cat("Scan Statistics:\n")
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print(summary(SS_H0))
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cat("Confusion Matrix:\n")
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print(confusionMatrix(factor(LS_H0$class), factor(SS_H0$class)))
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cat("---\n")
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}
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}
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#cat("- Empiric version:\n")
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Score = ScoreDistribEmpiric(lambda0, lambda1, NbSeq, T)
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Emp = EmpDistrib(lambda0,n_sample,T,tau)
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X_seq = Score$Score_X
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P_X = Score$P_X
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LS_H0 = LocalScoreMC(lambda0, lambda1, NbSeq, T, X_seq, P_X, tbe0)
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options(warn = -1) # Disable warnings print
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SS_H0 = ScanStatMC(NbSeq, T, tau, Emp, pp0)
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#cat("Local Score:\n")
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#print(summary(LS_H0))
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#cat("Scan Statistics:\n")
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#print(summary(SS_H0))
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#cat("Confusion Matrix:\n")
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#print(confusionMatrix(factor(LS_H0$class), factor(SS_H0$class)))
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#cat("- Elisa version:\n")
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Score = ScoreDistribElisa(lambda0, lambda1, T)
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Emp = EmpDistrib(lambda0,n_sample,T,tau)
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X_seq = Score$Score_X
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P_X = Score$P_X
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LS_H0 = LocalScoreMC(lambda0, lambda1, NbSeq, T, X_seq, P_X, tbe0)
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options(warn = -1) # Disable warnings print
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SS_H0 = ScanStatMC(NbSeq, T, tau, Emp, pp0)
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#cat("Local Score:\n")
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#print(summary(LS_H0))
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#cat("Scan Statistics:\n")
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#print(summary(SS_H0))
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#cat("Confusion Matrix:\n")
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print(confusionMatrix(factor(LS_H0$class), factor(SS_H0$class))$table)
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Sensitivity = c(Sensitivity,confusionMatrix(factor(LS_H0$class), factor(SS_H0$class))$byClass[1])
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Specificity = c(Specificity,confusionMatrix(factor(LS_H0$class), factor(SS_H0$class))$byClass[2])
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cat("---\n")
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}
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}
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}
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titleSens=TeX(paste(r'(Sensitivity for $\lambda_0=$)', lambda0))
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plot(x=accepted_lambda,y=Sensitivity, type='l', main = titleSens)
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titleSpec=TeX(paste(r'(Specificity for $\lambda_0=$)', lambda0))
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plot(x=accepted_lambda,y=Specificity, type='l', main = titleSpec)
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}
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}
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```
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```
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