Мой код слишком долго, чтобы вставить сюда, чтобы отправить по электронной почте [email protected] и я пошлю вам мой кодзаказа правильно используя `pattern`
Я написал код помощи Если функции и результаты каждого цикла if (212 из них) дают результат «n» или сумму лагов из acf-графика. Я хочу найти минимальное значение, а затем вызвать соответствующие команды в том, что если контур
Здесь ив выбрал несколько, если это петли, так что вы можете увидеть, как упорядочение работы
M<-matrix(c("08Q1","08Q2","08Q3","08Q4","09Q1","09Q2","09Q3","09Q4","10Q1","10Q2","10Q3","10Q4","11Q1","11Q2","11Q3","11Q4","12Q1","12Q2","12Q3","12Q4","13Q1","13Q2","13Q3","13Q4","14Q1","14Q2","14Q3",155782.698,159463.6534,172741.1256,204547.18,126049.3198,139881.9102,140747.2786,251962.9696,182444.2912,207780.8227,189251.1889,318053.6736,230569.1533,247826.8104,237019.5556,383909.5231,265145.4548,264816.362,239607.0146,436403.1441,276767.6893,286337.8543,270022.6845,444672.8604,263717.216,343143.9422,271701.7404),ncol=2,byrow=FALSE)
Nu <- M[, length(M[1,])]
Nu <- ts(Nu, deltat=deltaT, start = startY)
N<-log(Nu)
orderWA1<-c(0,0,0)
orderWS1<-c(0,0,0)
ArimaW1 <- Arima(N, order= orderWA1, seasonal=list(order=orderWS1), method="ML")
if(aslog=="y"){Arimafit<-exp(fitted(ArimaW1))}else{Arimafit<-fitted(ArimaW1)}
nnn<-c(N)
arimab<-c(Arimafit)
fullres<-nnn-arimab
v<-acf(fullres,plot=FALSE)
w<-pacf(fullres,plot=FALSE)
if(v$acf[2]>0.4|v$acf[2]<(-0.4)|v$acf[3]>0.4|v$acf[3]<(-0.4)|v$acf[4]>0.4|v$acf[4]<(-0.4)|v$acf[5]>0.4|v$acf[5]<(-0.4)|v$acf[6]>0.4|v$acf[6]<(-0.4)|v$acf[7]>0.4|v$acf[7]<(-0.4)|w$acf[1]>0.4|w$acf[1]<(-0.4)|w$acf[2]>0.4|w$acf[2]<(-0.4)|w$acf[3]>0.4|w$acf[3]<(-0.4)|w$acf[4]>0.4|w$acf[4]<(-0.4)|w$acf[5]>0.4|w$acf[5]<(-0.4)|w$acf[6]>0.4|w$acf[6]<(-0.4))
a01<-"n" else
{a01<-sum(abs(v$acf[2:7]))
b01<-sum(abs(w$acf[1:6]))}
orderWA2<-c(0,0,0)
orderWS2<-c(0,0,1)
ArimaW1 <- Arima(N, order= orderWA2, seasonal=list(order=orderWS2), method="ML")
if(aslog=="y"){Arimafit<-exp(fitted(ArimaW1))}else{Arimafit<-fitted(ArimaW1)}
nnn<-c(N)
arimab<-c(Arimafit)
fullres<-nnn-arimab
v<-acf(fullres,plot=FALSE)
w<-pacf(fullres,plot=FALSE)
if(v$acf[2]>0.4|v$acf[2]<(-0.4)|v$acf[3]>0.4|v$acf[3]<(-0.4)|v$acf[4]>0.4|v$acf[4]<(-0.4)|v$acf[5]>0.4|v$acf[5]<(-0.4)|v$acf[6]>0.4|v$acf[6]<(-0.4)|v$acf[7]>0.4|v$acf[7]<(-0.4)|w$acf[1]>0.4|w$acf[1]<(-0.4)|w$acf[2]>0.4|w$acf[2]<(-0.4)|w$acf[3]>0.4|w$acf[3]<(-0.4)|w$acf[4]>0.4|w$acf[4]<(-0.4)|w$acf[5]>0.4|w$acf[5]<(-0.4)|w$acf[6]>0.4|w$acf[6]<(-0.4))
a02<-"n" else
{a02<-sum(abs(v$acf[2:7]))
b02<-sum(abs(w$acf[1:6]))}
orderWA10<-c(0,0,0)
orderWS10<-c(1,0,0)
ArimaW1 <- Arima(N, order= orderWA10, seasonal=list(order=orderWS10), method="ML")
if(aslog=="y"){Arimafit<-exp(fitted(ArimaW1))}else{Arimafit<-fitted(ArimaW1)}
nnn<-c(N)
arimab<-c(Arimafit)
fullres<-nnn-arimab
v<-acf(fullres,plot=FALSE)
w<-pacf(fullres,plot=FALSE)
if(v$acf[2]>0.4|v$acf[2]<(-0.4)|v$acf[3]>0.4|v$acf[3]<(-0.4)|v$acf[4]>0.4|v$acf[4]<(-0.4)|v$acf[5]>0.4|v$acf[5]<(-0.4)|v$acf[6]>0.4|v$acf[6]<(-0.4)|v$acf[7]>0.4|v$acf[7]<(-0.4)|w$acf[1]>0.4|w$acf[1]<(-0.4)|w$acf[2]>0.4|w$acf[2]<(-0.4)|w$acf[3]>0.4|w$acf[3]<(-0.4)|w$acf[4]>0.4|w$acf[4]<(-0.4)|w$acf[5]>0.4|w$acf[5]<(-0.4)|w$acf[6]>0.4|w$acf[6]<(-0.4))
a10<-"n" else
{a10<-sum(abs(v$acf[2:7]))
b10<-sum(abs(w$acf[1:6]))}
orderWA11<-c(0,0,0)
orderWS11<-c(1,0,1)
ArimaW1 <- Arima(N, order= orderWA11, seasonal=list(order=orderWS11), method="ML")
if(aslog=="y"){Arimafit<-exp(fitted(ArimaW1))}else{Arimafit<-fitted(ArimaW1)}
nnn<-c(N)
arimab<-c(Arimafit)
fullres<-nnn-arimab
v<-acf(fullres,plot=FALSE)
w<-pacf(fullres,plot=FALSE)
if(v$acf[2]>0.4|v$acf[2]<(-0.4)|v$acf[3]>0.4|v$acf[3]<(-0.4)|v$acf[4]>0.4|v$acf[4]<(-0.4)|v$acf[5]>0.4|v$acf[5]<(-0.4)|v$acf[6]>0.4|v$acf[6]<(-0.4)|v$acf[7]>0.4|v$acf[7]<(-0.4)|w$acf[1]>0.4|w$acf[1]<(-0.4)|w$acf[2]>0.4|w$acf[2]<(-0.4)|w$acf[3]>0.4|w$acf[3]<(-0.4)|w$acf[4]>0.4|w$acf[4]<(-0.4)|w$acf[5]>0.4|w$acf[5]<(-0.4)|w$acf[6]>0.4|w$acf[6]<(-0.4))
a11<-"n" else
{a11<-sum(abs(v$acf[2:7]))
b11<-sum(abs(w$acf[1:6]))}
orderWA12<-c(0,0,0)
orderWS12<-c(1,0,2)
ArimaW1 <- Arima(N, order= orderWA12, seasonal=list(order=orderWS12), method="ML")
if(aslog=="y"){Arimafit<-exp(fitted(ArimaW1))}else{Arimafit<-fitted(ArimaW1)}
nnn<-c(N)
arimab<-c(Arimafit)
fullres<-nnn-arimab
v<-acf(fullres,plot=FALSE)
w<-pacf(fullres,plot=FALSE)
if(v$acf[2]>0.4|v$acf[2]<(-0.4)|v$acf[3]>0.4|v$acf[3]<(-0.4)|v$acf[4]>0.4|v$acf[4]<(-0.4)|v$acf[5]>0.4|v$acf[5]<(-0.4)|v$acf[6]>0.4|v$acf[6]<(-0.4)|v$acf[7]>0.4|v$acf[7]<(-0.4)|w$acf[1]>0.4|w$acf[1]<(-0.4)|w$acf[2]>0.4|w$acf[2]<(-0.4)|w$acf[3]>0.4|w$acf[3]<(-0.4)|w$acf[4]>0.4|w$acf[4]<(-0.4)|w$acf[5]>0.4|w$acf[5]<(-0.4)|w$acf[6]>0.4|w$acf[6]<(-0.4))
a12<-"n" else
{a12<-sum(abs(v$acf[2:7]))
b12<-sum(abs(w$acf[1:6]))}
orderWA100<-c(0,2,0)
orderWS100<-c(2,0,2)
ArimaW1 <- Arima(N, order= orderWA100, seasonal=list(order=orderWS100), method="ML")
if(aslog=="y"){Arimafit<-exp(fitted(ArimaW1))}else{Arimafit<-fitted(ArimaW1)}
nnn<-c(N)
arimab<-c(Arimafit)
fullres<-nnn-arimab
v<-acf(fullres,plot=FALSE)
w<-pacf(fullres,plot=FALSE)
if(v$acf[2]>0.4|v$acf[2]<(-0.4)|v$acf[3]>0.4|v$acf[3]<(-0.4)|v$acf[4]>0.4|v$acf[4]<(-0.4)|v$acf[5]>0.4|v$acf[5]<(-0.4)|v$acf[6]>0.4|v$acf[6]<(-0.4)|v$acf[7]>0.4|v$acf[7]<(-0.4)|w$acf[1]>0.4|w$acf[1]<(-0.4)|w$acf[2]>0.4|w$acf[2]<(-0.4)|w$acf[3]>0.4|w$acf[3]<(-0.4)|w$acf[4]>0.4|w$acf[4]<(-0.4)|w$acf[5]>0.4|w$acf[5]<(-0.4)|w$acf[6]>0.4|w$acf[6]<(-0.4))
a100<-"n" else
{a100<-sum(abs(v$acf[2:7]))
b100<-sum(abs(w$acf[1:6]))}
orderWA101<-c(0,2,1)
orderWS101<-c(0,0,0)
ArimaW1 <- Arima(N, order= orderWA101, seasonal=list(order=orderWS101), method="ML")
if(aslog=="y"){Arimafit<-exp(fitted(ArimaW1))}else{Arimafit<-fitted(ArimaW1)}
nnn<-c(N)
arimab<-c(Arimafit)
fullres<-nnn-arimab
v<-acf(fullres,plot=FALSE)
w<-pacf(fullres,plot=FALSE)
if(v$acf[2]>0.4|v$acf[2]<(-0.4)|v$acf[3]>0.4|v$acf[3]<(-0.4)|v$acf[4]>0.4|v$acf[4]<(-0.4)|v$acf[5]>0.4|v$acf[5]<(-0.4)|v$acf[6]>0.4|v$acf[6]<(-0.4)|v$acf[7]>0.4|v$acf[7]<(-0.4)|w$acf[1]>0.4|w$acf[1]<(-0.4)|w$acf[2]>0.4|w$acf[2]<(-0.4)|w$acf[3]>0.4|w$acf[3]<(-0.4)|w$acf[4]>0.4|w$acf[4]<(-0.4)|w$acf[5]>0.4|w$acf[5]<(-0.4)|w$acf[6]>0.4|w$acf[6]<(-0.4))
a101<-"n" else
{a101<-sum(abs(v$acf[2:7]))
b101<-sum(abs(w$acf[1:6]))}
l1<-mget(ls(pattern="^a\\d+"))
k<-unlist(l1)
j<-match(min(k),k)
orderWA<-paste("orderWA",j,sep="")
orderWS<-paste("orderWS",j,sep="")
if(orderWA=="orderWA1")
{orderWA<-orderWA1
orderWS<-orderWS1} else
if(orderWA=="orderWA2")
{orderWA<-orderWA2
orderWS<-orderWS2} else
if(orderWA=="orderWA10")
{orderWA<-orderWA10
orderWS<-orderWS10}else
if(orderWA=="orderWA11")
{orderWA<-orderWA11
orderWS<-orderWS11}else
if(orderWA=="orderWA12")
{orderWA<-orderWA12
orderWS<-orderWS12} else
if(orderWA=="orderWA100")
{orderWA<-orderWA100
orderWS<-orderWS100}else
if(orderWA=="orderWA101")
{orderWA<-orderWA101
orderWS<-orderWS101}else
NULL
Вот первый 16 результатов в качестве примера
> l1
$a01
[1] "n"
$a02
[1] "n"
$a03
[1] 1.210138
$a04
[1] "n"
$a05
[1] "n"
$a06
[1] "n"
$a07
[1] "n"
$a08
[1] "n"
$a09
[1] "n"
$a10
[1] "n"
$a100
[1] "n"
$a101
[1] "n"
$a102
[1] "n"
$a103
[1] "n"
$a104
[1] 0.8426679
$a105
[1] 0.7266627
Мне нужно, чтобы это было правильно заказать $ a01: $ A212 в противном случае я получить неправильные результаты, когда я называю соответствующую цифровую команду. например, если я называю l1 [11] я получаю $ A100 вместо $ a11
Лучше указать используемые пакеты. Напр. 'Arima'. Это из 'прогноза' – akrun
да извините, что пакеты, которые я использовал, являются сериями и прогнозами –
Некоторые объекты в коде не найдены. 'Ошибка в Arima (N, order = orderWA1, seasonal = list (order = orderWS1),: Объект 'N' не найден' Лучше будет показать воспроизводимый пример. – akrun