winbugs出现trap 66 postcondition violated，问题出在哪里？

peggyalways

10222

收藏 2011-11-30

请高手帮忙看看下面程序哪里出现了问题，在运行时出现trap 66 postcondition violated完整的程序见附件，非常感谢！！！模型代码：

cod.txt
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model {
for (i in 1:N) {Y ~ dbern(p)
   logit(p) <- beta0+beta1*x1+beta2*x2+beta3*x3+beta4*x4+beta5*x5+beta6*x6+beta7*x7+beta8*x8+beta9*x9+beta10*x10+beta11*x11+ U +S
   eta <- S+U
   U ~ dnorm(0.0,prec.u)
               }
S[1:N] ~ car.normal(adj[], weights[], num[], prec.s)
for(k in 1:sumNumNeigh) {weights[k] <- 1 }
#priors for regression
beta0 ~ dflat()
beta1 ~ dnorm(0.0,0.1)
beta2 ~ dnorm(0.0,0.1)
beta3 ~ dnorm(0.0,0.1)
beta4 ~ dnorm(0.0,0.1)
beta5 ~ dnorm(0.0,0.1)
beta6 ~ dnorm(0.0,0.1)
beta7 ~ dnorm(0.0,0.1)
beta8 ~ dnorm(0.0,0.1)
beta9 ~ dnorm(0.0,0.1)
beta10 ~ dnorm(0.0,0.1)
beta11 ~ dnorm(0.0,0.1)
prec.u ~ dgamma(0.02,0.02) #prior unstructured precision
prec.s ~ dgamma(0.02,0.02) #prior spatial precision
}

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沙发

peggyalways

2011-11-30 15:18:21

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藤椅

henrykaka

2011-12-1 14:06:45

我也出现trap66的错误了，哪位高手给看看啊

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板凳

epoh

2011-12-1 19:47:40

change the prior by increasing the prior precision
can solve the problem.(trap 66 postcondition violated)

不理解为何要底下这2句,
  eta <- S+U--没用上
  U ~ dnorm(0.0,prec.u)--为何要用 U,去掉就可执行
#########
model {
for (i in 1:N) {Y ~ dbern(p)
   logit(p) <- beta0+beta1*x1+beta2*x2+beta3*x3+beta4*x4+beta5*x5+beta6*x6+beta7*x7+beta8*x8+beta9*x9+beta10*x10+beta11*x11+ S
      #  eta <- S+U
      #  U ~ dnorm(0.0,prec.u)
               }
S[1:N] ~ car.normal(adj[], weights[], num[], prec.s)
for(k in 1:sumNumNeigh) {weights[k] <- 1 }
#priors for regression
beta0 ~ dflat()
beta1 ~ dnorm(0.0,1.0E-4)
beta2 ~ dnorm(0.0,1.0E-4)
beta3 ~ dnorm(0.0,1.0E-4)
beta4 ~ dnorm(0.0,1.0E-4)
beta5 ~ dnorm(0.0,1.0E-4)
beta6 ~ dnorm(0.0,1.0E-4)
beta7 ~ dnorm(0.0,1.0E-4)
beta8 ~ dnorm(0.0,1.0E-4)
beta9 ~ dnorm(0.0,1.0E-4)
beta10 ~ dnorm(0.0,1.0E-4)
beta11 ~ dnorm(0.0,1.0E-4)
prec.u ~ dgamma(0.5,0.05) #prior unstructured precision
prec.s ~ dgamma(0.5,0.05) #prior spatial precision
}

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报纸

zhangtao

2011-12-2 09:45:36

epoh 发表于 2011-12-1 19:47
change the prior by increasing the prior precision
can solve the problem.(trap 66 postcondition vio ...
model {
for (i in 1:N) {Y ~ dbern(p)
   logit(p) <- beta0+beta1*x1+beta2*x2+beta3*x3+beta4*x4+beta5*x5+beta6*x6+beta7*x7+beta8*x8+beta9*x9+beta10*x10+beta11*x11+ U +S
   #eta <- S+U
   U ~ dnorm(0.0,prec.u)
               }
S[1:N] ~ car.normal(adj[], weights[], num[], prec.s)
for(k in 1:sumNumNeigh) {weights[k] <- 1 }
#priors for regression
beta0 ~ dflat()
beta1 ~ dnorm(0.0,0.1)
beta2 ~ dnorm(0.0,0.1)
beta3 ~ dnorm(0.0,0.1)
beta4 ~ dnorm(0.0,0.1)
beta5 ~ dnorm(0.0,0.1)
beta6 ~ dnorm(0.0,0.1)
beta7 ~ dnorm(0.0,0.1)
beta8 ~ dnorm(0.0,0.1)
beta9 ~ dnorm(0.0,0.1)
beta10 ~ dnorm(0.0,0.1)
beta11 ~ dnorm(0.0,0.1)
prec.u ~ dgamma(0.02,0.02) #prior unstructured precision
prec.s ~ dgamma(0.02,0.02) #prior spatial precision
}
epoh老师，您好！
   我觉得这个eta, u and s应该是有用的，没有看到原文章，
不能确定，但是从我看的相关文献来看，这个eta,u and s的设定
应该是必不可少的。
根据您的意见，就是注释了u and s, eta，程序也无法出结果，
结果是no monitor set。
我用多种方法进行修改，还是无法出结果。
TRAP 66  (postcondition violated)

UpdaterRejection.Updater.MCMC [000005B5H]
.deriv REAL -1.014142135623731
.e REAL 0.0
.f UpdaterRejection.Factory [01352A70H]
.i INTEGER 0
.k INTEGER 0
.lambdaL REAL 5.254810133233113E-308
.lambdaR REAL 0.9860550753913473
.left REAL 1.424162706015637E-306
.leftStar REAL 5.257051011885653E-308
.linear REAL 0.0
.logFleft REAL 0.0
.logFmode REAL -1.540743955509789E-37
.logFright REAL -inf
.logQ REAL 0.0
.mode REAL 5.551115123125783E-17
.mu REAL 0.0
.oldValue REAL 1.0
.overRelax BOOLEAN FALSE
.pL REAL 5.25983340698144E-308
.pM REAL 5.254672730896907E-308
.pR REAL 2.077575945862661E+175
.prec REAL -1.0E-4
.prior GraphStochastic.Node [01205880H]
.rand REAL 1.602184914048457E-306

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地板

zhangtao

2011-12-2 09:50:23

Bugs users,

Why might I be getting "trap 66 postcondition violated" in a
random-effects logit? I don't get it until 30K-40K total
iterations (whether one chain at 30K or 35K, or 4 chains at
10K) after about 22 hours on a PII-400 machine running NT.

Might this just be some kind of results storage problem?
The model is generating 7 individual (level 1) coefficients
for each of 4230 cases, so it's quite big.

Thanks for any help

Fred

-----------------------------------------
Fred Cutler

Social Sciences and Humanities Research Council of Canada
Post-Doctoral Fellow
Department of Political Science
University of British Columbia
and
Research Affiliate
Institute for the Humanities
University of Manitoba

(204) 474-9063
[log in to unmask]
home.cc.umanitoba.ca/~cutlerfe

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7楼

zhangtao

2011-12-2 09:50:58

https://www.jiscmail.ac.uk/cgi-b ... amp;S=&P=174521

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8楼

epoh

2011-12-2 15:52:46

zhangtao 发表于 2011-12-2 09:50
https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=ind00&L=BUGS&F=&S=&P=174521
底下是我昨日稍試一下的結果:
example scotland就是加了b,沒加u
  log(mu) <- log(E) + alpha0 + alpha1 * X/10 + b
  # CAR prior distribution for random effects:
b[1:N] ~ car.normal(adj[], weights[], num[], tau)
如果要加u,可能要自行多試修改prior
#######################
Inference for Bugs model at "car.bug", fit using WinBUGS,
1 chains, each with 1000 iterations (first 500 discarded)
n.sims = 500 iterations saved
         mean    sd 2.5%    25%    50%    75% 97.5%
beta0    1.782  0.191 1.393 1.662 1.804 1.912 2.128
beta1    -0.191  0.070  -0.326  -0.236  -0.189  -0.139  -0.059
beta2    0.248  0.075 0.110 0.194 0.246 0.303 0.403
beta3    0.217  0.089 0.047 0.158 0.214 0.287 0.389
beta4    0.045  0.096  -0.136  -0.021 0.044 0.106 0.233
beta5    -0.287  0.112  -0.512  -0.362  -0.289  -0.215  -0.067
beta6    -0.199  0.126  -0.440  -0.285  -0.197  -0.110 0.025
beta7    -0.050  0.146  -0.336  -0.154  -0.053 0.052 0.224
beta8    -0.073  0.149  -0.362  -0.168  -0.072 0.036 0.213
beta9    0.362  0.165 0.034 0.262 0.359 0.472 0.675
beta10    0.154  0.167  -0.171 0.061 0.153 0.262 0.491
beta11 -0.446  0.191  -0.831  -0.570  -0.446  -0.319  -0.062
prec.s 10.853  8.021 3.519 5.727 7.275  12.262  33.848
deviance 556.036 13.821 529.200 546.900 555.850 566.900 582.205

DIC info (using the rule, pD = Dbar-Dhat)
pD = 55.8 and DIC = 611.8
DIC is an estimate of expected predictive error (lower deviance is better).

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9楼

peggyalways

2011-12-2 15:56:34

epoh 发表于 2011-12-1 19:47
change the prior by increasing the prior precision
can solve the problem.(trap 66 postcondition vio ...
谢谢您～
我按照您给出的代码还是出错，prec。u是得定义的，因为那个在变量U里面，我改动prec的值能跑到3000次，但是不收敛，
不知道这种情况您知道怎么回事吗？

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10楼

epoh

2011-12-2 18:42:52

peggyalways 发表于 2011-12-2 15:56
谢谢您～
我按照您给出的代码还是出错，prec。u是得定义的，因为那个在变量U里面，我改动prec的值能跑到 ...
程序已经由不能执行
到能执行3000次
显示prior的设置很重要
接着下来你可能要
花点时间自行分别试试
不同的parameter要有不同的prior
而且是分批完成
不要一次抓足11个parameters

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11楼

zhangtao

2011-12-5 08:56:50

我认为最好能程序能执行2万次，取掉前后500次的结果，然后再做分析，
这样的结果比较准确，做出的结果比较漂亮。
另外，如果能把你模型的想法说清楚，例如：为什么U是变量？你的方程设定的目标及需要的结果？
相关的参考文献？
这样epoh老师就可以给您做更多的帮助！
另外，如果您把问题解决了，希望能说说如何解决的！

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12楼

libaijun19

2012-2-8 19:11:52

epoh 发表于 2011-12-1 19:47
change the prior by increasing the prior precision
can solve the problem.(trap 66 postcondition vio ...
老师您好！
我最近在做 winbugs 程序。出现了个问题想请教下您，model ，到初始化都可以，但是在update的过程中弹出 trap框，就走不了，我的代码入下：
# model volatility
{

d0<-exp(2*delta0)
d1<--0.5*d0

yisigma20<-1/(h0*(v/(v-2)))
y0~dt(alpha,yisigma20,v)
ymean[1]<-alpha+beta*y0
yisigma2[1]<-1/(h[1]*(v/(v-2)))
y[1]~dt( ymean[1],yisigma2[1],v)
for(t in 2:100){
      ymean[t]<-alpha+beta*y[t-1]+pow(y[t-1],rou)*sqrt(y[t]-alpha-beta*y[t-1]-mm0/y[t-1])+jump[t]*bernsong[t]+mm0/y[t-1]
      yisigma2[t]<-1/(h[t]*(v/(v-2)))
         y[t]~dt( ymean[t],yisigma2[t],v)
      jump[t]<-exp(jump0[t])-1
      jump0[t]~dnorm(d1,d0)
      bernsong[t]~dbern(k)
                     }


alpha~dnorm(0,1)
beta~dnorm(0,1)
mu~dunif(0,1)
phi1~dunif(0,1)
phi2~dunif(0,1)
v~dunif(2,128)
mm0~dnorm(0,0.01)
delta0~dnorm(-3.07,0.149)
k~dbeta(2,100)
rou~dunif(0.5,1.9)
#deltaf~dgamma(2.5,0.025)

h0<-mu
h[1]<-mu+phi1*h0+phi2*(y0-alpha)*(y0-alpha)
h[2]<-mu+phi1*h[1]+phi2*(y[1]-alpha-beta*y0-mm0/y0)*(y[1]-alpha-beta*y0-mm0/y0)
   for(j in 3:100){
h[j]<-mu+phi1*h[j-1]+phi2*(y[j-1]-alpha-beta*y[j-2]-mm0/y[j-2])*(y[j-1]-alpha-beta*y[j-2]-mm0/y[j-2])

               }
}
#data
list(
y=c(0.025594,0.025354,0.025174,0.025104,0.024904,0.024774,0.024664,0.024504,0.024414,0.024204,0.024244,0.024174,0.024164,0.023994,0.023974,0.023935,0.023865,0.023755,0.023725,0.023761,0.023725,0.023645,0.023625,0.023585,0.023575,0.023555,0.023395,0.023275,0.023295,0.023175,0.023225,0.023055,0.023115,0.023065,0.023005,0.023015,0.022995,0.023025,0.023075,0.023075,0.023005,0.023085,0.023285,0.023585,0.024114,0.025564,0.025534,0.024714,0.025124,0.025774,0.024694,0.025244,0.025244,0.025224,0.025044,0.024984,0.024964,0.024864,0.024654,0.024484,0.024234,0.024084,0.023954,0.023875,0.023735,0.023782,0.023825,0.023605,0.023415,0.023465,0.023265,0.022975,0.022952,0.022994,0.023021,0.023022,0.023018,0.022969,0.022919,0.022854,0.022777,0.02268,0.022535,0.022209,
0.022192,0.021606,0.021446,0.021381,0.021385,0.021385,0.021396,0.021379,0.021378,0.021385,0.021389,0.021386,0.021385,0.021372,0.021357,0.02135,0.021353,0.021363,0.019693,0.01958,0.019566,
0.019545,0.019533,0.019524,0.019517,0.019503,0.019501,0.019501,0.019505,0.019519,0.01951,0.019513,0.019514,0.019508,0.019508,
0.0195,0.019489,0.019491,0.019491,0.019489,0.019497,0.019501,
0.0195,0.019689,0.020278,0.020634,0.020979,0.022827,0.022944,
0.022492,0.022396,0.02236,0.022307,0.022313,0.022294,0.02228,
0.022282,0.022408,0.022956,0.023491,0.023576,0.023033,0.022741,0.022504,0.022607,0.022995,0.022628,0.022385,0.021467,0.020755,0.020536,0.020116,0.01978,0.0197,0.019526,0.019494,0.019601,
0.019922,0.02013,0.020396,0.021034,0.021326,0.021486,0.021695,
0.022439,0.02274,0.022395,0.021765,0.021436,0.021186,0.020986,
0.020876,0.021486,0.028602,0.028292,0.028142,0.028052,0.030411,0.03219,0.030481,0.029102,0.03275,0.030681,0.026543,0.022125,
0.021755,0.021546,0.021356,0.021296,0.021366,0.021286,0.021026,0.028432,0.027243,0.022915,0.021785,0.021116,0.020416,0.019656,0.019116,0.018517,0.018807,0.018627,0.018317,0.021055,0.024426,0.022749,0.021811,0.021112,0.021439,0.024878,0.023885,0.024174,0.024414,0.023922,0.022992,0.022073,0.024226,0.024064,0.022972,0.022419,0.022364,0.022226,0.021386,0.021051,0.020906,0.021331,0.022087,0.021574,0.020867,0.02218,0.021706,0.021381,0.019912,
0.019451,0.019079,0.018404,0.018383,0.018321,0.019086,0.018479,0.018247,0.018348,0.018419,0.023626,0.019763,0.018647,0.018393,0.018288,0.016901,0.015092,0.014132,0.01309,0.012702,0.012243,
0.01219,0.011573,0.011322,0.011241,0.010971,0.010929,0.010881,
0.010893,0.011288,0.011559,0.01191,0.012548,0.012289,0.012488,
0.012469,0.012356,0.012285,0.012075,0.012181,0.012016,0.01175,
0.011452,0.011195,0.011105,0.011194,0.011057,0.011184,0.011681,0.01315,0.015559,0.016228,0.015571,0.015064,0.015065,0.014853,
0.01456,0.014911,0.020212,0.014196,0.013372,0.013155,0.014121,
0.014222,0.014122,0.016274,0.016177,0.015668,0.015528,0.017336,0.021099,0.01748,0.016547,0.01615,0.017351,0.018108,0.020561,
0.020907,0.020805,0.020303,0.019952,0.026532,0.026846,0.026069,0.025307,0.023957,0.022225,0.022274,0.021171,0.020823,0.021921,0.023957,0.022473,0.024816,0.022967,0.024184,0.03184,0.038217,
0.038793,0.023691,0.022367,0.02034,0.021035,0.019499,0.014814,
0.013888,0.015965,0.020641,0.019712,0.041984,0.020652,0.016793,0.014783,0.015104,0.018557,0.019563,0.021791,0.024811,0.042072,0.032851,0.030155,0.025315,0.023425,0.028143,0.022956,0.043142,0.021866,0.029018,0.023655,0.030976,0.021117,0.02117,0.020254,
0.022554,0.019712,0.02429,0.024474,0.02852,0.057212,0.068364,
0.020222,0.022238,0.025188,0.102,0.030743,0.027296,0.020917,
0.027458,0.030807,0.024537,0.033433,0.021542,0.024812,0.027769,0.020272,0.026349,0.035388,0.029447,0.025864,0.038202,0.028134,0.02368,0.025489,0.024155,0.023903,0.028004,0.028383,0.028898,
0.029521,0.039154,0.034845,0.031309,0.033352,0.033399,0.031508,0.032519,0.029273,0.027659,0.031611,0.030267,0.037872,0.031574,0.027895,0.030999,0.029985,0.029655,0.030072,0.033736,0.034274,0.034304,0.030686,0.029982,0.027985,0.02792,0.027099,0.025185,
0.020991,0.01854,0.015396,0.014029,0.011759,0.009366,0.009163,
0.010209,0.009421,0.009681,0.009936,0.009447,0.009352,0.009498,0.009843,0.009761,0.009569,0.009503,0.009224,0.009729,0.009397,0.00956,0.009699,0.00945,0.009403,0.009932,0.011692,0.012441,
0.012281,0.015623,0.017149,0.01936,0.016028,0.014119,0.012642,
0.013953,0.017329,0.014394,0.015442,0.016266,0.016988,0.015278,0.014218,0.013396,0.014414,0.014832,0.014482,0.014609,0.015016,0.014813,0.014558,0.014625,0.013896,0.014224,0.014804,0.018636,0.017263,0.02556,0.016758,0.01693,0.016747,0.01638,0.015559,
0.015876,0.016926,0.016782,0.016995,0.016003,0.017085,0.017483,0.01825,0.023053,0.022063,0.027267,0.027321,0.03134,0.025967,
0.022847,0.018609,0.01779,0.01673,0.016934,0.017406,0.017477,
0.022609,0.023424,0.02289,0.01997,0.026823,0.019344,0.019384,
0.020161,0.016141,0.016975,0.020921,0.0272,0.033317,0.024854,
0.037192,0.05419,0.051686,0.028044,0.02529,0.074909,0.080092,
0.039141,0.02842,0.031447,0.022087,0.020523,0.020241,0.022814,
0.027831,0.024809,0.023617,0.035424,0.029438,0.02767,0.036063,
0.040768,0.043924,0.034699,0.046058,0.067023,0.084078,0.05893,
0.061433,0.041315,0.054042,0.050954,0.030142,0.030842,0.045151,0.040731,0.042123,0.036292,0.033175,0.04121,0.050204,0.031432,
0.034444,0.050413,0.034991,0.035037,0.037059,0.040388,0.036987,0.035227,0.030753,0.037346,0.055984

   )
)
#initial values
list(alpha=0.020676,beta=0.518890865,mu=0.3,phi1=0.1,phi2=0.1,v=3,y0=1,mm1=0,rou=1.4,delta0=0,k=0.5,mm0=0.00000000000000322)

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13楼

libaijun19

2012-2-8 19:16:36

zhangtao 发表于 2011-12-2 09:45
model {
for (i in 1:N) {Y ~ dbern(p)
   logit(p)
zhangtao老师您好！
做 winbugs 程序。出现了个问题想请教下您，model ，到初始化都可以，但是在update的过程中弹出 trap框，就走不了，上面也问了epoh老师，希望您们能帮助下我，非常谢谢了。我的代码入下：
# model volatility
{

d0<-exp(2*delta0)
d1<--0.5*d0

yisigma20<-1/(h0*(v/(v-2)))
y0~dt(alpha,yisigma20,v)
ymean[1]<-alpha+beta*y0
yisigma2[1]<-1/(h[1]*(v/(v-2)))
y[1]~dt( ymean[1],yisigma2[1],v)
for(t in 2:100){
      ymean[t]<-alpha+beta*y[t-1]+pow(y[t-1],rou)*sqrt(y[t]-alpha-beta*y[t-1]-mm0/y[t-1])+jump[t]*bernsong[t]+mm0/y[t-1]
      yisigma2[t]<-1/(h[t]*(v/(v-2)))
         y[t]~dt( ymean[t],yisigma2[t],v)
      jump[t]<-exp(jump0[t])-1
      jump0[t]~dnorm(d1,d0)
      bernsong[t]~dbern(k)
                     }


alpha~dnorm(0,1)
beta~dnorm(0,1)
mu~dunif(0,1)
phi1~dunif(0,1)
phi2~dunif(0,1)
v~dunif(2,128)
mm0~dnorm(0,0.01)
delta0~dnorm(-3.07,0.149)
k~dbeta(2,100)
rou~dunif(0.5,1.9)
#deltaf~dgamma(2.5,0.025)

h0<-mu
h[1]<-mu+phi1*h0+phi2*(y0-alpha)*(y0-alpha)
h[2]<-mu+phi1*h[1]+phi2*(y[1]-alpha-beta*y0-mm0/y0)*(y[1]-alpha-beta*y0-mm0/y0)
   for(j in 3:100){
h[j]<-mu+phi1*h[j-1]+phi2*(y[j-1]-alpha-beta*y[j-2]-mm0/y[j-2])*(y[j-1]-alpha-beta*y[j-2]-mm0/y[j-2])

               }
}
#data
list(
y=c(0.025594,0.025354,0.025174,0.025104,0.024904,0.024774,0.024664,0.024504,0.024414,0.024204,0.024244,0.024174,0.024164,0.023994,0.023974,0.023935,0.023865,0.023755,0.023725,0.023761,0.023725,0.023645,0.023625,0.023585,0.023575,0.023555,0.023395,0.023275,0.023295,0.023175,0.023225,0.023055,0.023115,0.023065,0.023005,0.023015,0.022995,0.023025,0.023075,0.023075,0.023005,0.023085,0.023285,0.023585,0.024114,0.025564,0.025534,0.024714,0.025124,0.025774,0.024694,0.025244,0.025244,0.025224,0.025044,0.024984,0.024964,0.024864,0.024654,0.024484,0.024234,0.024084,0.023954,0.023875,0.023735,0.023782,0.023825,0.023605,0.023415,0.023465,0.023265,0.022975,0.022952,0.022994,0.023021,0.023022,0.023018,0.022969,0.022919,0.022854,0.022777,0.02268,0.022535,0.022209,
0.022192,0.021606,0.021446,0.021381,0.021385,0.021385,0.021396,0.021379,0.021378,0.021385,0.021389,0.021386,0.021385,0.021372,0.021357,0.02135,0.021353,0.021363,0.019693,0.01958,0.019566,
0.019545,0.019533,0.019524,0.019517,0.019503,0.019501,0.019501,0.019505,0.019519,0.01951,0.019513,0.019514,0.019508,0.019508,
0.0195,0.019489,0.019491,0.019491,0.019489,0.019497,0.019501,
0.0195,0.019689,0.020278,0.020634,0.020979,0.022827,0.022944,
0.022492,0.022396,0.02236,0.022307,0.022313,0.022294,0.02228,
0.022282,0.022408,0.022956,0.023491,0.023576,0.023033,0.022741,0.022504,0.022607,0.022995,0.022628,0.022385,0.021467,0.020755,0.020536,0.020116,0.01978,0.0197,0.019526,0.019494,0.019601,
0.019922,0.02013,0.020396,0.021034,0.021326,0.021486,0.021695,
0.022439,0.02274,0.022395,0.021765,0.021436,0.021186,0.020986,
0.020876,0.021486,0.028602,0.028292,0.028142,0.028052,0.030411,0.03219,0.030481,0.029102,0.03275,0.030681,0.026543,0.022125,
0.021755,0.021546,0.021356,0.021296,0.021366,0.021286,0.021026,0.028432,0.027243,0.022915,0.021785,0.021116,0.020416,0.019656,0.019116,0.018517,0.018807,0.018627,0.018317,0.021055,0.024426,0.022749,0.021811,0.021112,0.021439,0.024878,0.023885,0.024174,0.024414,0.023922,0.022992,0.022073,0.024226,0.024064,0.022972,0.022419,0.022364,0.022226,0.021386,0.021051,0.020906,0.021331,0.022087,0.021574,0.020867,0.02218,0.021706,0.021381,0.019912,
0.019451,0.019079,0.018404,0.018383,0.018321,0.019086,0.018479,0.018247,0.018348,0.018419,0.023626,0.019763,0.018647,0.018393,0.018288,0.016901,0.015092,0.014132,0.01309,0.012702,0.012243,
0.01219,0.011573,0.011322,0.011241,0.010971,0.010929,0.010881,
0.010893,0.011288,0.011559,0.01191,0.012548,0.012289,0.012488,
0.012469,0.012356,0.012285,0.012075,0.012181,0.012016,0.01175,
0.011452,0.011195,0.011105,0.011194,0.011057,0.011184,0.011681,0.01315,0.015559,0.016228,0.015571,0.015064,0.015065,0.014853,
0.01456,0.014911,0.020212,0.014196,0.013372,0.013155,0.014121,
0.014222,0.014122,0.016274,0.016177,0.015668,0.015528,0.017336,0.021099,0.01748,0.016547,0.01615,0.017351,0.018108,0.020561,
0.020907,0.020805,0.020303,0.019952,0.026532,0.026846,0.026069,0.025307,0.023957,0.022225,0.022274,0.021171,0.020823,0.021921,0.023957,0.022473,0.024816,0.022967,0.024184,0.03184,0.038217,
0.038793,0.023691,0.022367,0.02034,0.021035,0.019499,0.014814,
0.013888,0.015965,0.020641,0.019712,0.041984,0.020652,0.016793,0.014783,0.015104,0.018557,0.019563,0.021791,0.024811,0.042072,0.032851,0.030155,0.025315,0.023425,0.028143,0.022956,0.043142,0.021866,0.029018,0.023655,0.030976,0.021117,0.02117,0.020254,
0.022554,0.019712,0.02429,0.024474,0.02852,0.057212,0.068364,
0.020222,0.022238,0.025188,0.102,0.030743,0.027296,0.020917,
0.027458,0.030807,0.024537,0.033433,0.021542,0.024812,0.027769,0.020272,0.026349,0.035388,0.029447,0.025864,0.038202,0.028134,0.02368,0.025489,0.024155,0.023903,0.028004,0.028383,0.028898,
0.029521,0.039154,0.034845,0.031309,0.033352,0.033399,0.031508,0.032519,0.029273,0.027659,0.031611,0.030267,0.037872,0.031574,0.027895,0.030999,0.029985,0.029655,0.030072,0.033736,0.034274,0.034304,0.030686,0.029982,0.027985,0.02792,0.027099,0.025185,
0.020991,0.01854,0.015396,0.014029,0.011759,0.009366,0.009163,
0.010209,0.009421,0.009681,0.009936,0.009447,0.009352,0.009498,0.009843,0.009761,0.009569,0.009503,0.009224,0.009729,0.009397,0.00956,0.009699,0.00945,0.009403,0.009932,0.011692,0.012441,
0.012281,0.015623,0.017149,0.01936,0.016028,0.014119,0.012642,
0.013953,0.017329,0.014394,0.015442,0.016266,0.016988,0.015278,0.014218,0.013396,0.014414,0.014832,0.014482,0.014609,0.015016,0.014813,0.014558,0.014625,0.013896,0.014224,0.014804,0.018636,0.017263,0.02556,0.016758,0.01693,0.016747,0.01638,0.015559,
0.015876,0.016926,0.016782,0.016995,0.016003,0.017085,0.017483,0.01825,0.023053,0.022063,0.027267,0.027321,0.03134,0.025967,
0.022847,0.018609,0.01779,0.01673,0.016934,0.017406,0.017477,
0.022609,0.023424,0.02289,0.01997,0.026823,0.019344,0.019384,
0.020161,0.016141,0.016975,0.020921,0.0272,0.033317,0.024854,
0.037192,0.05419,0.051686,0.028044,0.02529,0.074909,0.080092,
0.039141,0.02842,0.031447,0.022087,0.020523,0.020241,0.022814,
0.027831,0.024809,0.023617,0.035424,0.029438,0.02767,0.036063,
0.040768,0.043924,0.034699,0.046058,0.067023,0.084078,0.05893,
0.061433,0.041315,0.054042,0.050954,0.030142,0.030842,0.045151,0.040731,0.042123,0.036292,0.033175,0.04121,0.050204,0.031432,
0.034444,0.050413,0.034991,0.035037,0.037059,0.040388,0.036987,0.035227,0.030753,0.037346,0.055984

   )
)
#initial values
list(alpha=0.020676,beta=0.518890865,mu=0.3,phi1=0.1,phi2=0.1,v=3,y0=1,mm1=0,rou=1.4,delta0=0,k=0.5,mm0=0.00000000000000322)

万分谢谢老师

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14楼

zhangtao

2012-2-10 09:12:36

undefined real result

Math.Sqrt [00000120H]
.x REAL -13.18914409111797
GraphStack.Node.Value [00000FE9H]
.i INTEGER 19
.j INTEGER 6
.k INTEGER 5
.node GraphStack.Node [011462E0H]
.op INTEGER 23
.p GraphNodes.Node [01176BB0H]
.top INTEGER 2
.work ARRAY 10 OF REAL Elements
GraphT.Node.Likelihood [00000712H]
.lambda REAL 0.0
.logLambda REAL 0.01
.logLikelihood REAL 0.2231299172210023
.logTau REAL 3.065529831432839E-303
.mu REAL 5.255114088343153E-308
.node GraphT.Node [0117E100H]
.tau REAL 8.487983573935575E-314
.x REAL 0.02135
GraphStochastic.Node.Conditional [000004BCH]
.likelihood GraphStochastic.List [0125E990H]
.log REAL -2.489347997952568E-4
.node GraphStochastic.Node [01176BB0H]
UpdaterMetnormal.Updater.MCMC [000001B8H]
.alpha REAL 2.122045197035791E-314
.newDen REAL 5.338947044029618E-315
.newVal REAL 0.2231299172210023
.oldDen REAL -30.40847927996916
.oldVal REAL -0.08627110712337649
.overRelax BOOLEAN FALSE
.prior GraphStochastic.Node [01176BB0H]
.rate REAL 4.839867702782698E-276
.res INTEGER 0
.updater UpdaterMetnormal.Updater [012782A0H]
BugsUpdaters.Updater.MCMC [000005B8H]
.chain INTEGER 0
.depth INTEGER 1
.i INTEGER 0
.name BugsNames.Name [01123220H]
.ok BOOLEAN TRUE
.overRelax BOOLEAN FALSE
.res INTEGER 0
.size INTEGER 1
.string ARRAY 120 OF CHAR 0B5B0X, 93ECX, 5946X, 3FD0X, 6108X, 498FX, 6C22X, 0BFE3X, 75C2X, 811BX, 3980X, 0C001X, 0AD14X, 1C94X, 83E4X, 3FA4X, 10B2X, 6365X, 321EX, 43E5X, 3550X, 3F9AX, 8E50X, 97C9X, 838EX, 3FE0X, 6E2X, 3233X, 4E98X, 3FC3X, 8E50X, 97C9X ...
.updater BugsUpdaters.Updater [01278250H]
BugsSampler.MCMC [0000036DH]
.chain INTEGER 0
.cursor BugsSampler.List [012782D0H]
.depth INTEGER 1
.depth0 INTEGER 1
.depth1 INTEGER 4
.offset INTEGER 18928464
.ok BOOLEAN TRUE
.overRelax BOOLEAN FALSE
.updater BugsUpdaters.Updater [01278250H]
BugsCmds.Action.Step [00000B1FH]
.a BugsCmds.Action [011C68B0H]
.chain INTEGER 0
.i INTEGER 15
.j INTEGER 0
.numChains INTEGER 1
BugsCmds.Action.Do [00000C58H]
.a BugsCmds.Action [011C68B0H]
.elapsedTime LONGINT 63E521F163E40384H
.profileList ARRAY 2024 OF CHAR "" ...
.res INTEGER 1639404127
.s ARRAY 120 OF CHAR "" ...
Services.Exec [00000136H]
.a Services.Action [011C68B0H]
.t POINTER [63E40384H]
Services.IterateOverActions [000002F4H]
.p Services.Action [011C68B0H]
.t POINTER NIL
.time LONGINT 969515
Services.StdHook.Step [0000034DH]
.h Services.StdHook [0111E380H]
HostWindows.Idle [00004A86H]
.focus BOOLEAN FALSE
.tick Controllers.TickMsg Fields
.w HostWindows.Window NIL
HostMenus.TimerTick [00003422H]
.lParam INTEGER 0
.ops Controllers.PollOpsMsg Fields
.wParam INTEGER 1
.wnd INTEGER 131790
Kernel.Try [00003A61H]
.a INTEGER 131790
.b INTEGER 1
.c INTEGER 0
.h PROCEDURE HostMenus.TimerTick
HostMenus.ApplWinHandler [00003841H]
.Proc PROCEDURE NIL
.hit BOOLEAN FALSE
.lParam INTEGER 0
.message INTEGER 275
.res INTEGER 65758
.s ARRAY 256 OF SHORTCHAR 1X ...
.w INTEGER 1629125088
.wParam INTEGER 1
.wnd INTEGER 131790
<system> (pc=77D18733H,  fp=0022FB84H)
<system> (pc=77D18815H,  fp=0022FBECH)
<system> (pc=77D189CCH,  fp=0022FC4CH)
<system> (pc=77D196C6H,  fp=0022FC5CH)
HostMenus.Loop [00003BDEH]
.done BOOLEAN FALSE
.f SET {0..5}
.n INTEGER 2
.res INTEGER 0
.w HostWindows.Window NIL
Kernel.Start [00002B8CH]
.code PROCEDURE HostMenus.Loop

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15楼

zhangtao

2012-2-10 09:14:33

由于这个程序问题比较多，等我好好思考试试再说！

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16楼

zhangtao

2012-2-10 09:17:06

model is syntactically correct
expected variable name
model is syntactically correct
data loaded
model compiled
expected variable name
undefined variable
initial values generated, model initialized
expected variable name
alpha
alpha GraphNormal.StdNode
alpha UpdaterMetnormal.Updater
alpha    2.745372382736294
初步判断是某一个变量没有定义，得好好找找

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17楼

xuan1993

2013-7-20 09:54:29

zhangtao 发表于 2012-2-10 09:17
model is syntactically correct
expected variable name
model is syntactically correct
老师，您好，我也存在类似问题，模型前面几步骤都OK，就是最后不能updating了，请麻烦老师看看问题出在哪里？怎么样可以修改正确了？谢谢老师。model
{
alpha ~ dnorm(5,50)
blpha ~ dnorm(5,50)
for( i in 1 : N ) {
   p ~ dbeta(alpha,blpha)
}
for( i in 1 : N ) {
   r ~ dbin(p,n)
}
feta <- 1 / (1 + alpha + blpha)
}
list(N=47,n=c(1,9,10,6,17,12,9,5,5,2,1,10,1,15,9,12,4,7,8,8,9,2,12,8,4,5,8,9,13,3,2,9,5,3,6,2,3,4,7,5,6,8,6,5,4,4,1),r=c(0,0,0,1,2,1,1,0,1,1,1,2,1,2,1,2,1,2,5,1,4,1,0,1,2,4,2,4,6,1,2,1,1,3,4,2,1,3,3,2,3,5,3,3,3,3,1),p=c(0,0,0,0.167,0.118,0.083,0.111,0,0.2,0.5,1,0.2,1,0.133,0.111,0.167,0.25,0.286,0.625,0.125,0.444,0.5,0,0.125,0.5,0.8,0.25,0.444,0.462,0.333,1,0.111,0.2,1,0.667,1,0.333,0.75,0.429,0.4,0.5,0.625,0.5,0.6,0.75,0.75,1))
list(alpha=1,blpha=1)
前面所有的步骤都OK 但是最后updating时出现了错误：“sorry something went wrong in procedure UpdaterStd.Sample”，也就是通常说的最后无法updating。
本文来自: 人大经济论坛 S-Plus&R专版版，详细出处参考： https://bbs.pinggu.org/forum.php? ... =2541191&page=1

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18楼

dgdzc

2014-5-24 21:06:43

epoh 发表于 2011-12-2 18:42
程序已经由不能执行
到能执行3000次
显示prior的设置很重要
您好，请问对prior的设置有没有一些可以遵循的规则？
谢谢！

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