winbugs做ar(2)模型的代码求助 不太会啊 太折磨人了

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model
{
for (i in 3: n)
{
         Ymean[i]<- beta0*  Y[i-1] +beta1* Y[i-2] ;
         Y[i]~dnorm(Ymean[i],tau);
       }
       beta0~dnorm(0,1.0E-6);
       beta1~dnorm(0,1.0E-6);
       tau~dgamma(1.0E-3,1.0E-3);
       sigma<-1/sprt(tau);
}

#data
list(Y=c(284,324,422,675,1038,1250,1428,1645,1709,2701,4148,4603,2737,2417,
2533,3153,3587,3638,3130,3077,3465,3872,4419,4791,5215,5010,5382,
5633,6110,6557,6423,6482,6306,6784,7022,7383,18117,22012,19758,
20847,18665,15908,16064,20751,25915,27279,27161,28819,24826,21363,
21596,25902,22583,22411,23320,24812,29300,32900,37900,42799,42016,
48702,54012,58639,70909,75328))

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关键词：winbugs WINBUG BUGS Win bug 模型

一阶差分平稳后的数据
40,98,253,363,212,178,217,64,992,1447,455,-1866,-320,116,620,434,
51,-508,-53,388,407,547,372,424,-205,372,251,477,447,-134,59,-176,
478,238,361,10734,3895,-2254,1089,-2182,-2757,156,4687,5164,1364,
-118,1658,-3993,-3463,233,4306,-3319,-172,909,1492,4488,3600,5000,
4899,-783,6686,5310,4627,12270,4419

model;
{
   for( i in 3 : N ) {
      Y[i] ~ dnorm(Ymean[i],tau)
   }
   for( i in 3 : N ) {
      Ymean[i] <- beta1 * Y[i - 1] +beta2*Y[i-2]
   }
   beta1 ~ dnorm( 0.0,1.0E-6)
   beta2 ~ dnorm( 0.0,1.0E-6)
   for( i in 3 : N ) {
      Y[i - 1] ~ dnorm( 0.0,1.0E-6)
      Y[i - 2] ~ dnorm( 0.0,1.0E-6)
   }
   tau ~ dgamma(0.001,0.001)
   sigma <- 1 / sqrt(tau)
}

#data
list(
    Y=c(40,98,253,363,212,178,217,64,992,1447,455,-1866,-320,116,620,434,
51,-508,-53,388,407,547,372,424,-205,372,251,477,447,-134,59,-176,
478,238,361,10734,3895,-2254,1089,-2182,-2757,156,4687,5164,1364,
-118,1658,-3993,-3463,233,4306,-3319,-172,909,1492,4488,3600,5000,
4899,-783,6686,5310,4627,12270,4419)
N=65)

list(
  beta1=0,
  beta2=0,
  tau=1
)

您好，请问您这个问题解决了吗？有同样的疑惑

豪烈翼龙 发表于 2017-5-27 11:35
您好，请问您这个问题解决了吗？有同样的疑惑

model;
{
   for( i in 3 : N ) {
      Y ~ dnorm(Ymean,tau)
   }
   for( i in 3 : N ) {
      Ymean <- beta1 * Y[i - 1] +beta2*Y[i-2]
   }
   beta1 ~ dnorm( 0.0,1.0E-6)
   beta2 ~ dnorm( 0.0,1.0E-6)
   for( i in 3 : N ) {
      Y[i - 1] ~ dnorm( 0.0,1.0E-6)
      Y[i - 2] ~ dnorm( 0.0,1.0E-6)
   }
   tau ~ dgamma(0.001,0.001)
   sigma <- 1 / sqrt(tau)
}

#data
list(
    Y=c(284,324,422,675,1038,1250,1428,1645,1709,2701,4148,4603,2737,2417,
2533,3153,3587,3638,3130,3077,3465,3872,4419,4791,5215,5010,5382,
5633,6110,6557,6423,6482,6306,6784,7022,7383,18117,22012,19758,
20847,18665,15908,16064,20751,25915,27279,27161,28819,24826,21363,
21596,25902,22583,22411,23320,24812,29300,32900,37900,42799,42016,
48702,54012,58639,70909,75328),
N=66)

list(
  beta1=0,
  beta2=0,
  tau=1
)


这是ar(2)的程序  你看下吧

红藤green 发表于 2017-5-27 18:12
model;
{
   for( i in 3 : N ) {

你好，你这个程序是解决什么问题的，刚接触，不太清楚我的问题该用什么模型