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Scan-Statistics-Project-4Y-INSA
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Paul-Corbalan 2022-04-19 08:10:45 +02:00
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Comparaison_of_methods.rmd
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@ -91,7 +91,7 @@ Plot_CDF <- function(lambda,n_sample,T,tau){
return(Emp) return(Emp)
} }
``` ```
### 2.1 Test of $\mathcal{H}_0: \lambda=\lambda_0$ against $\mathcal{H}_0: \lambda=\lambda_1$, where $\lambda_1 > \lambda_0$ ### 2.1. Test of $\mathcal{H}_0: \lambda=\lambda_0$ against $\mathcal{H}_0: \lambda=\lambda_1$, where $\lambda_1 > \lambda_0$
In this part, we will test different values for $\lambda_0$ and $\lambda_1$, and compute the probability of occurrence of a certain scan statistic. In this part, we will test different values for $\lambda_0$ and $\lambda_1$, and compute the probability of occurrence of a certain scan statistic.
```{r} ```{r}
@ -226,7 +226,7 @@ ScanStatMC <- function(NbSeq, T, tau, Emp, pp0){
``` ```
## 3. Local score ## 3. Local score
### Distribution of scores via Monte Carlo ### 3.1. Distribution of scores via Monte Carlo
```{r} ```{r}
ComputeE <- function(lambda0, lambda1){ ComputeE <- function(lambda0, lambda1){
E = 1 E = 1
@ -305,7 +305,7 @@ mtext("Distribution des scores via la méthode théorique",side=1,line=4,col="re
``` ```
### Local score calculation ### 3.2. Local score calculation
```{r} ```{r}
LocalScoreMC <- function(lambda0, lambda1, NbSeq, T, X_seq, P_X, tbe0){ LocalScoreMC <- function(lambda0, lambda1, NbSeq, T, X_seq, P_X, tbe0){
E = ComputeE(lambda0, lambda1) E = ComputeE(lambda0, lambda1)