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Transition to functions and experience plan

This commit is contained in:
Paul-Corbalan 2022-04-10 22:59:22 +02:00
parent 178e4c41ef
commit e5e98522b2
1 changed files with 39 additions and 26 deletions
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55
Comparaison_of_methods.rmd
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@ -83,7 +83,6 @@ EmpDistrib <- function(lambda, n_sample,T,tau){
} }
``` ```
```{r} ```{r}
library("latex2exp")
Plot_CDF <- function(lambda,n_sample,T,tau){ Plot_CDF <- function(lambda,n_sample,T,tau){
Emp=EmpDistrib(lambda,n_sample,T,tau) Emp=EmpDistrib(lambda,n_sample,T,tau)
title=TeX(paste(r'(Cumulative distribution function for $\lambda=$)', lambda)) title=TeX(paste(r'(Cumulative distribution function for $\lambda=$)', lambda))
@ -257,13 +256,13 @@ ComputeE <- function(lambda0, lambda1){
``` ```
```{r} ```{r}
ScoreDistrib <- function(lambda0, lambda1, n_sample, T){ ScoreDistrib <- function(lambda0, lambda1, T){
E = ComputeE(lambda0, lambda1) E = ComputeE(lambda0, lambda1)
ppH0 = PoissonProcess(lambda0,T) ppH0 = PoissonProcess(lambda0,T)
n1 = length(ppH0) n1 = length(ppH0)
tbe0 = ppH0[2:n1]-ppH0[1:n1-1] tbe0 = ppH0[2:n1]-ppH0[1:n1-1]
print(ks.test(tbe0, 'exp')) # print(ks.test(tbe0, 'exp'))
X = floor(E*(log(lambda1/lambda0)+(lambda0-lambda1)*tbe0)) # ne pas mettre le floor ni le E (certes égale à 1) X = floor(E*(log(lambda1/lambda0)+(lambda0-lambda1)*tbe0)) # ne pas mettre le floor ni le E (certes égale à 1)
@ -281,29 +280,43 @@ ScoreDistrib <- function(lambda0, lambda1, n_sample, T){
### Calcul du local score ### Calcul du local score
```{r} ```{r}
library("localScore") LocaScoreMC <- function(lambda0, lambda1, E, T){
library(Rcpp) pvalue = c()
E = 10 X = c()
pvalue=c()
X=c()
Score = ScoreDistrib(lambda0, lambda1, n_sample, 10**4) Score = ScoreDistrib(lambda0, lambda1, T)
xp = Score$X xp = Score$X
P_X = Score$P_X P_X = Score$P_X
min_X = min(xp) min_X = min(xp)
max_X = max(xp) max_X = max(xp)
for (i in 1:NbSeqH0){ for (i in 1:NbSeqH0){
x = floor(E*log(dexp(tbe0[[i]], rate = lambda1)/dexp(tbe0[[i]], rate = lambda0))) x = floor(E*log(dexp(tbe0[[i]], rate = lambda1)/dexp(tbe0[[i]], rate = lambda0)))
X=c(X,x) X = c(X,x)
LS=localScoreC(x)$localScore[1] LS = localScoreC(x)$localScore[1]
result = daudin(localScore = LS, score_probabilities = P_X, sequence_length = length(x), sequence_min = min_X, sequence_max = max_X) daudin_result = daudin(localScore = LS, score_probabilities = P_X, sequence_length = length(x), sequence_min = min_X, sequence_max = max_X)
pvalue = c(pvalue,result) pvalue = c(pvalue, daudin_result)
}
LS_H0=data.frame(num=1:NbSeqH0, pvalue_scan=pvalue, class=(pvalue<0.05))
return(LS_H0)
} }
LS_H0=data.frame(num=1:NbSeqH0, pvalue_scan=pvalue, class=(pvalue<0.05))
LS_H0
``` ```
```{r}
E = 10
for (T in 10**(2:5)){
for (lambda0 in c(1, 2, 10)){
for (lambda1 in c(4, 20, 100, 1000)){
if (lambda0 < lambda1){
cat("T = ", T, ", lambda0 = ", lambda0, ", lambda1 = ", lambda1, "\n", sep = "")
LS_H0 = LocaScoreMC(lambda0, lambda1, E, T)
print(summary(LS_H0))
cat("---\n")
}
}
}
}
```