Fusion of codes
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@ -354,30 +354,27 @@ plot_graph_distrib_score(distrib_score_theo, distrib_score_mc)
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LocalScoreMC <- function(lambda0, lambda1, NbSeq, T, X_seq, P_X, tbe0){
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E = ComputeE(lambda0, lambda1)
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pvalue = c()
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X = c()
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min_X = min(X_seq)
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max_X = max(X_seq)
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NbSeq.NonNulles = 0
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for (i in 1:NbSeq){
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x = floor(E*log(dexp(tbe0[[i]], rate = lambda1)/dexp(tbe0[[i]], rate = lambda0)))
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if (length(x)!=0){
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X = c(X,x)
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LS = localScoreC(x)$localScore[1]
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daudin_result = daudin(localScore = LS, score_probabilities = P_X, sequence_length = length(x), sequence_min = min_X, sequence_max = max_X)
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options(warn = -1) # Disable warnings print
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pvalue = c(pvalue, daudin_result)
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NbSeq.NonNulles = NbSeq.NonNulles + 1
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}
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}
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LS_H0=data.frame(num=1:NbSeq.NonNulles, pvalue_scan=pvalue, class=(pvalue<0.05)*1)
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LS_H0=data.frame(num=1:NbSeq.NonNulles, pvalue_scan=pvalue, class=as.numeric(pvalue<0.05))
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return(LS_H0)
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}
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```
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## 4. Experience plan for comparaison
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```{r}
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CompareMethods <- function(lambda0, lambda1, NbSeq, T, tau){
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CompareMethods <- function(lambda0, lambda1, NbSeq, T, tau_scan, tau_H1){
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if (lambda0 < lambda1){
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cat("For T = ", T, ", Nb = ", NbSeq, ", lambda0 = ", lambda0, " and lambda1 = ", lambda1, ":\n", sep = "")
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@ -395,7 +392,7 @@ CompareMethods <- function(lambda0, lambda1, NbSeq, T, tau){
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tbe0[[i]] = tbei
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#Simulation for sequences under H1
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ppj1 = SimulationH1(lambda0, lambda1, T, 3)
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ppj1 = SimulationH1(lambda0, lambda1, T, tau_H1)
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nj = length(ppj1)
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pp1[[i]] = ppj1
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tbej = ppj1[2:nj]-ppj1[1:nj-1]
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@ -409,9 +406,9 @@ CompareMethods <- function(lambda0, lambda1, NbSeq, T, tau){
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LS_obtained = c(LS_H0$class, LS_H1$class)
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options(warn = -1)
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Emp = EmpDistrib(lambda0,10**5,T,tau)
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SS_H0 = ScanStatMC(NbSeq, T, tau, Emp, pp0)
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SS_H1 = ScanStatMC(NbSeq, T, tau, Emp, pp1)
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Emp = EmpDistrib(lambda0, 10**5, T, tau_scan)
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SS_H0 = ScanStatMC(NbSeq, T, tau_scan, Emp, pp0)
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SS_H1 = ScanStatMC(NbSeq, T, tau_scan, Emp, pp1)
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SS_obtained = c(SS_H0$class, SS_H1$class)
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@ -450,7 +447,8 @@ CompareMethods <- function(lambda0, lambda1, NbSeq, T, tau){
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```{r}
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NbSeq = 10**4
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T = 10
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tau = 2
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tau_scan = 2
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tau_H1 = 3
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list_of_lambda = list()
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list_of_lambda[[1]] = c(1, 3)
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@ -465,7 +463,7 @@ legend_list = c()
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for (Lambda in list_of_lambda){
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lambda0 = Lambda[1]
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lambda1 = Lambda[2]
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result = CompareMethods(lambda0, lambda1, NbSeq, T, tau)
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result = CompareMethods(lambda0, lambda1, NbSeq, T, tau_scan)
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title_ROC = TeX(paste(r'(ROC curve for several values of $\lambda_0$ and $\lambda_1$)'))
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perfSS = result[1]
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