A.6
Fase II para n=5
ni=1000 resp<-array(0,dim=c(ni,1)) resp.final<-array(0,dim=c(4,4)) colnames(resp.final) <- c(”0”,”0.5”,”1.0”,”1.5”) rownames(resp.final) <- c(”0”,”0.5”,”1.0”,”1.5”) sim1=c(0,0.5,1,1.5) sim2=c(0,0.5,1,1.5) for(ia in 1:4){ delta1<-sim1[ia] for(ib in 1:4){ delta2<-sim2[ib] for(i1 in 1:ni){ n=1000 delta1=0.0 delta2=0.0 ro=0.0 fi=0.0 x1.coluna.d<-rnorm(n,0,1) x1.d=rbind(x1.coluna.d)+delta1
X1.d = matrix(x1.d, ncol =1, byrow=n) x2.residuo.d<-rnorm(n,0,1)
x2.res.d=rbind(x2.residuo.d)
vetor.residuo.x2.d<-matrix(x2.res.d, ncol=1, byrow=n) x3.residuo.d<-rnorm(n,0,1)
x3.res.d=rbind(x3.residuo.d)
vetor.residuo.x3.d<-matrix(x3.res.d, ncol=1, byrow=n) x4.residuo.d<-rnorm(n,0,1)
x4.res.d=rbind(x4.residuo.d)
vetor.residuo.x4.d<-matrix(x4.res.d, ncol=1, byrow=n) x5.residuo.d<-rnorm(n,0,1)
x5.res.d=rbind(x5.residuo.d)
vetor.residuo.x5.d<-matrix(x5.res.d, ncol=1, byrow=n)
A.6 Fase II para n=5 69
v.x2.d<-c(1:n)
X2.d<-matrix(v.x2.d, ncol=1, byrow=n) for(i in 1:n){
X2.d[i,]<-delta1+fi*(X1.d[i, ]-delta1)+vetor.residuo.x2.d[i, ] }
# Criando coluna de dados x3
v.x3.d<-c(1:n)
X3.d<-matrix(v.x3.d, ncol=1, byrow=n) for(i in 1:n){
X3.d[i,]<-delta1+fi*(X2.d[i, ]-delta1)+vetor.residuo.x3.d[i, ] }
# Criando coluna de dados x4
v.x4.d<-c(1:n)
X4.d<-matrix(v.x4.d, ncol=1, byrow=n) for(i in 1:n){
X4.d[i,]<-delta1+fi*(X3.d[i, ]-delta1)+vetor.residuo.x4.d[i, ] }
# Criando coluna de dados x5
v.x5.d<-c(1:n)
X5.d<-matrix(v.x4.d, ncol=1, byrow=n) for(i in 1:n){ X5.d[i,]<-delta1+fi*(X4.d[i, ]-delta1)+vetor.residuo.x5.d[i, ] } A1.d=cbind(X1.d,X2.d,X3.d,X4.d,X5.d) A1=A1.d y1.residuo.d<-rnorm(n,0,1) y1.res.d=rbind(y1.residuo.d)
vetor.residuo.y1.d<-matrix(y1.res.d, ncol=1, byrow=n) y2.residuo.d<-rnorm(n,0,1)
y2.res.d=rbind(y2.residuo.d)
A.6 Fase II para n=5 70
y3.residuo.d<-rnorm(n,0,1) y3.res.d=rbind(y3.residuo.d)
vetor.residuo.y3.d<-matrix(y3.res.d, ncol=1, byrow=n) y4.residuo.d<-rnorm(n,0,1)
y4.res.d=rbind(y4.residuo.d)
vetor.residuo.y4.d<-matrix(y4.res.d, ncol=1, byrow=n) y5.residuo.d<-rnorm(n,0,1)
y5.res.d=rbind(y5.residuo.d)
vetor.residuo.y5.d<-matrix(y5.res.d, ncol=1, byrow=n)
# Criando coluna de dados y1
v.y1.d<-c(1:n)
Y1.d<-matrix(v.y1.d, ncol=1, byrow=n) for(i in 1:n){
Y1.d[i,]<-(ro*(X1.d[i, ])+vetor.residuo.y1.d[i, ])+delta2 }
v.y2.d<-c(1:n)
Y2.d<-matrix(v.y2.d, ncol=1, byrow=n) for(i in 1:n){
Y2.d[i,]<-(ro*(X2.d[i, ])+vetor.residuo.y2.d[i, ])+delta2 }
v.y3.d<-c(1:n)
Y3.d<-matrix(v.y3.d, ncol=1, byrow=n) for(i in 1:n){
Y3.d[i,]<-(ro*(X3.d[i, ])+vetor.residuo.y3.d[i, ])+delta2 }
v.y4.d<-c(1:n)
Y4.d<-matrix(v.y4.d, ncol=1, byrow=n) for(i in 1:n){
Y4.d[i,]<-(ro*(X4.d[i, ])+vetor.residuo.y4.d[i, ])+delta2 }
v.y5.d<-c(1:n)
Y5.d<-matrix(v.y5.d, ncol=1, byrow=n) for(i in 1:n){
A.6 Fase II para n=5 71
}
B1.d=cbind(Y1.d,Y2.d,Y3.d,Y4.d,Y5.d) B1=B1.d
# GERADO OS VALORES COM DESLOCAMENTO DE M´EDIA
# UTILIZAMOS OS LIMITES DE CONTROLE E MATRIZ DE COVARI ˆANCIA ENCONTRADOS NO PROCESSO EM CONTROLE
#Inserindo algoritmo para deslocamento de m´edia
xbarra= ybarra= S11= S12= S22= lsc.medio= T2.deslocadovetor=c(1:n)
T2.deslocado<-matrix(T2.deslocadovetor, ncol=1, byrow=n)
for(i in 1:n){ T2.deslocado[i,1]<-(5/((S11∗S12)−(S222 )))∗((S12∗((mean(A1.d[i, ])−xbarra)2 ))+ (S11∗((mean(B1.d[i, ])−ybarra)2 ))−(2∗S22∗((mean(A1.d[i, ])−xbarra)∗(mean(B1.d[i, ])− ybarra)))) } NMA=0 cont=0 vetor<-array(2000,dim=c(n,1)) NMA<-array(10000,dim=c(n,1)) for(i in 1:n){ if(T2.deslocado[i,1]>lsc.medio){ vetor[i,1]<-T2.deslocado[i,1] NMA[i,1]<-i cont=min(NMA) } } cont=min(NMA)
A.6 Fase II para n=5 72 resp[i1,1]<-cont r<-(mean(resp[resp!=10000])) resp.final[ia,ib]=r } print(resp.final[1,1]) } }
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