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Scan-Statistics-Project-4Y-INSA
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Merge branch 'main' of https://github.com/Paul-Corbalan/Scan-Statistics-Project-4Y-INSA

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Paul-Corbalan 2022-05-08 19:11:15 +02:00
parent d98b5eb3c5 00642de65a
commit dcec5bc0a7
2 changed files with 17 additions and 1 deletions
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17
Comparaison_of_methods.rmd
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@ -273,6 +273,7 @@ ScoreDistribEmpiric <- function(lambda0, lambda1, n_sample, T){
``` ```
```{r} ```{r}
lambda0=5 lambda0=5
lambda1=7 lambda1=7
distrib_mc=ScoreDistribEmpiric(lambda0,lambda1,10000,T) distrib_mc=ScoreDistribEmpiric(lambda0,lambda1,10000,T)
@ -289,15 +290,18 @@ print(E)
barplot(distrib_mc[,2]) barplot(distrib_mc[,2])
``` ```
```{r} ```{r}
ScoreDistribTheo <- function(lambda0, lambda1, T){ ScoreDistribTheo <- function(lambda0, lambda1, T){
E = ComputeE(lambda0, lambda1) E = ComputeE(lambda0, lambda1)
score_max = floor(E*log(lambda1/lambda0)) score_max = floor(E*log(lambda1/lambda0))
## score_min compute ## score_min compute
score_min_c = floor(E*log(lambda1/lambda0)+E*(lambda0-lambda1)*T) score_min_c = floor(E*log(lambda1/lambda0)+E*(lambda0-lambda1)*T)
l = seq(score_min_c,score_max,1) l = seq(score_min_c,score_max,1)
borne_inf = (l-E*log(lambda1/lambda0))/(E*(lambda0-lambda1)) borne_inf = (l-E*log(lambda1/lambda0))/(E*(lambda0-lambda1))
borne_sup = (l+1-E*log(lambda1/lambda0))/(E*(lambda0-lambda1)) borne_sup = (l+1-E*log(lambda1/lambda0))/(E*(lambda0-lambda1))
@ -311,6 +315,16 @@ ScoreDistribTheo <- function(lambda0, lambda1, T){
``` ```
```{r} ```{r}
T=10
distrib_score_mc=ScoreDistribEmpiric(2,3,10000,T)
distrib_score_theo=ScoreDistribElisa(2,3,T)
distrib_score_mc
distrib_score_theo
distrib_score_mc = ScoreDistribEmpiric(2,3,10000,T) distrib_score_mc = ScoreDistribEmpiric(2,3,10000,T)
distrib_score_theo = ScoreDistribTheo(2,3,T) distrib_score_theo = ScoreDistribTheo(2,3,T)
@ -320,6 +334,7 @@ plot_graph_distrib_score <- function(distrib_score_theo, distrib_score_mc){
#diff_distrib_score=abs(distrib_score_mc[,2]-distrib_score_theo[,2]) #diff_distrib_score=abs(distrib_score_mc[,2]-distrib_score_theo[,2])
#par(mfrow = c(1,2)) #par(mfrow = c(1,2))
barplot(distrib_score_mc[,2],col="blue",axes=F) barplot(distrib_score_mc[,2],col="blue",axes=F)
mtext("Distribution of scores via Monte Carlo",side=1,line=2.5,col="blue") mtext("Distribution of scores via Monte Carlo",side=1,line=2.5,col="blue")
@ -329,10 +344,10 @@ plot_graph_distrib_score <- function(distrib_score_theo, distrib_score_mc){
mtext("Distribution of scores using the theoretical method",side=1,line=4,col="red") mtext("Distribution of scores using the theoretical method",side=1,line=4,col="red")
} }
plot_graph_distrib_score(distrib_score_theo, distrib_score_mc) plot_graph_distrib_score(distrib_score_theo, distrib_score_mc)
``` ```
### 3.2. 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){

1
Dataset_study.rmd
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@ -505,6 +505,7 @@ x.verif=seq(range(x)[1],range(x)[2],1)
#dist.theo.scores=lambda0*exp(-lambda0*(A*x.verif-B)) #dist.theo.scores=lambda0*exp(-lambda0*(A*x.verif-B))
#dist.theo.scores #dist.theo.scores
dist.emp.scores dist.emp.scores
barplot(dist.emp.scores)
``` ```