Cahnge lambda numbers for hypothesis
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				@ -140,14 +140,14 @@ PoissonProcess <- function(lambda,T) {
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lambda1=2
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lambda2=3
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lambda0=2
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lambda1=3
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Ti=10
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pp1=PoissonProcess(lambda1,Ti)
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pp1=PoissonProcess(lambda0,Ti)
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print(pp1)
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plot(c(0,pp1),0:length(pp1),type="s",xlab="time t",ylab="number of events by time t")
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pp2=PoissonProcess(lambda2,Ti)
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pp2=PoissonProcess(lambda1,Ti)
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print(pp2)
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plot(c(0,pp2),0:length(pp2),type="s",xlab="time t",ylab="number of events by time t")
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@ -160,9 +160,9 @@ n2=length(pp2)
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tbe2=pp2[2:n2]-pp2[1:n2-1]
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tbe2
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ks.test(tbe1,pexp,lambda1, alternative="two.sided")
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ks.test(tbe1,pexp,lambda0, alternative="two.sided")
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ks.test(tbe2,pexp,lambda2, alternative="two.sided")
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ks.test(tbe2,pexp,lambda1, alternative="two.sided")
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@ -177,13 +177,13 @@ Je reprends votre code pour faire un data set :
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```{r}
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# Etape 1 : simu Poisson process sous H0
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ppH0=PoissonProcess(lambda1,Ti)
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ppH0=PoissonProcess(lambda0,Ti)
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ppH0
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length(ppH0)
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# Etape 2 : creation d'un segment sous H1
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tau= 2.5 # longeur de l'intervalle modifie, a fortiori tau < Ti
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ppH1.segt=PoissonProcess(lambda2,tau)
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ppH1.segt=PoissonProcess(lambda1,tau)
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ppH1.segt
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length(ppH1.segt)
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