你的数据里哪里有pop这个变量啊

实在不好意思，现在把数据上传了data1。
请您帮助一下

我是按照给的例子，但是不知道怎么样把自己的数据运用上去，是求mass~length的线性回归，用分层的方法，group是层次。不知道怎么样把数据利用上去，还麻烦您指导一下，也不知道pop这么样可以不可以？

要是这个例子会用了，下面自己利用学习和运用就通顺多了，现在卡在这里两天了，请您多费心，不胜感激

library(R2WinBUGS)
library(lattice)
library(coda)
dat=read.csv("c:/data.csv")
attach(dat)
setwd("c:/")

sink("c:/lme.model1.txt")
cat("
model {

# Priors
for (i in 1:ngroups){
alpha ~ dnorm(mu.int, tau.int) # Random intercepts
}

mu.int ~ dnorm(0, 0.001) # Mean hyperparameter for random intercepts
tau.int <- 1 / (sigma.int * sigma.int)
sigma.int ~ dunif(0, 100) # SD hyperparameter for random intercepts

beta ~ dnorm(0, 0.001) # Common slope
tau <- 1 / ( sigma * sigma) # Residual precision
sigma ~ dunif(0, 100) # Residual standard deviation

# Likelihood
for (i in 1:n) {
mass ~ dnorm(mu, tau) # The random variable
mu <- alpha[pop] + beta* length # Expectation
}
}
",fill=TRUE)
sink()


# Bundle data
win.data <- list(mass = as.numeric(mass), pop = as.numeric(pop),
length = length, ngroups = max(as.numeric(pop)), n = 255)

# Inits function
inits <- function(){list(alpha = rnorm(win.data$ngroups, 0, 2), beta = rnorm(1, 1, 1),
mu.int = rnorm(1, 0, 1), sigma.int = rlnorm(1), sigma = rlnorm(1))}

# Parameters to estimate
parameters <- c("alpha", "beta", "mu.int", "sigma.int", "sigma")

# MCMC settings
ni <- 2000
nb <- 500
nt <- 2
nc <- 3

# Start Gibbs sampling
out <- bugs(win.data, inits, parameters, "lme.model1.txt", n.thin=nt,
n.chains=nc, n.burnin=nb, n.iter=ni, debug = TRUE)

# Inspect results
print(out, dig = 3)

It's OK!

我试试
谢谢你

好像算起来时间会很长

现在mass是因变量，length是自变量，ngroups是区组，那pop是什么？是mass和length的总个数吗？

忘记告诉你了，winbugs很快就计算结束了，结束后需要关掉winbugs，r软件才能进行后续的运算

楼主应该搞清楚多水平模型的概念，pop再次应该是水平1的变量

Thank you to noting me. But to be honest, it is a long time since I use WinBUGS, thus, I can't help you. Sorry for this.

大侠，你能帮我是试试吗？怎么把pop改成水平1的变量（mass是因变量，length是自变量，ngroups是区组）
数据改成什么样子才能算出来？这几天都没做出来，如果可以的话你用附件的数据试试，告诉我一下怎么做，现在头都大了，怎么都做不出来了。

出自Introduction to WinBUGS for Ecologists这本书的12章（麻烦您了，你费心看看怎么可以做出来，如果这个麻烦的话，你那有现成的例子让我用也可以，就是利用winbug算混合模型，截距、斜率、截距和斜率分别作为随机变量时怎么拟合出来，感觉他给的例子太麻烦，你那有现成的就更好了）
Chapter 12: Linear mixed-effects model
全部代码

截距及系数的随机效应模型，与单个截距效应模型相似，在指定先验后，将pop的脚标加上即可mu[i] <- alpha[pop[i]] + beta[pop[i]]* length[i] ，这本书的的p159-12.4.2的模型即可，并不复杂，你可以模仿
如果pop对应你的数据每个不同的分组。

还是没这么明白，pop和group怎么设置，您能帮用我的data1 数据试试这个例子吗？我没看懂你的回复，基础太差，希望您可以用我的data，做出个成功的例子，教教我，谢谢您

library(R2WinBUGS)
library(lattice)
library(coda)
dat=read.csv("c:/data.csv")
attach(dat)
setwd("c:/")

sink("lme.model2.txt")
cat("
model {

# Priors
for (i in 1:6){               
    alpha[i] ~ dnorm(mu.int, tau.int)        # Random intercepts
    beta[i] ~ dnorm(mu.slope, tau.slope)# Random slopes
}

mu.int ~ dnorm(0, 0.001)                # Mean hyperparameter for random intercepts
tau.int <- 1 / (sigma.int * sigma.int)
sigma.int ~ dunif(0, 100)                # SD hyperparameter for random intercepts

mu.slope ~ dnorm(0, 0.001)                # Mean hyperparameter for random slopes
tau.slope <- 1 / (sigma.slope * sigma.slope)
sigma.slope ~ dunif(0, 100)                # SD hyperparameter for slopes

tau <- 1 / ( sigma * sigma)                # Residual precision
sigma ~ dunif(0, 100)                        # Residual standard deviation

# Likelihood
for (i in 1:n) {
    mass[i] ~ dnorm(mu[i], tau)
    mu[i] <- alpha[pop[i]] + beta[pop[i]]* length[i]
}
}
",fill=TRUE)
sink()

# Bundle data
win.data <- list(mass = as.numeric(mass), pop = pop,
length = length, n =length(mass))

# Inits function
inits <- function(){ list(alpha = rnorm(6, 0, 2), beta = rnorm(6, 10, 2),
mu.int = rnorm(1, 0, 1), sigma.int = rlnorm(1), mu.slope = rnorm(1, 0, 1),
sigma.slope = rlnorm(1), sigma = rlnorm(1))}

# Parameters to estimate
parameters <- c("alpha", "beta", "mu.int", "sigma.int", "mu.slope", "sigma.slope", "sigma")

# MCMC settings
ni <- 2000
nb <- 500
nt <- 2
nc <- 3

# Start Gibbs sampling
out <- bugs(win.data, inits, parameters, "lme.model2.txt", n.thin=nt,
n.chains=nc, n.burnin=nb, n.iter=ni, debug = TRUE)

print(out, dig = 2)

这是结果

按照你index代码运行到out<-bugs(..)
结束后需要关掉winbugs，r软件才能进行后续的运算
出现了：
Error in bugs.run(n.burnin, bugs.directory, WINE = WINE, useWINE = useWINE,  :
  Look at the log file and
try again with 'debug=TRUE' to figure out what went wrong within Bugs.
难道是winbug 的问题，这怎么回事呢?
自己太菜了，您都做出来了，都交给我了，我还没做出来，到底是什么原因呢？

用我给你的那个数据

就是用你给我的数据呀，结果是
winbug中的log显示：
command #Bugs:inits cannot be executed (is greyed out)
gen.inits()
command #Bugs:gen.inits cannot be executed (is greyed out)
thin.updater(2)
update(250)
command #Bugs:update cannot be executed (is greyed out)
set(alpha)
command #Bugs:set cannot be executed (is greyed out)
set(beta)
command #Bugs:set cannot be executed (is greyed out)
set(mu.int)
command #Bugs:set cannot be executed (is greyed out)
set(sigma.int)
command #Bugs:set cannot be executed (is greyed out)
set(mu.slope)
command #Bugs:set cannot be executed (is greyed out)
set(sigma.slope)
command #Bugs:set cannot be executed (is greyed out)
set(sigma)
command #Bugs:set cannot be executed (is greyed out)
set(deviance)
command #Bugs:set cannot be executed (is greyed out)
dic.set()
command #Bugs:dic.set cannot be executed (is greyed out)
update(750)
command #Bugs:update cannot be executed (is greyed out)
coda(*,C:/Users/www/AppData/Local/Temp/RtmpIHbeKi/coda)
command #Bugs:coda cannot be executed (is greyed out)
stats(*)
command #Bugs:stats cannot be executed (is greyed out)
dic.stats()

DIC
history(*,C:/Users/www/AppData/Local/Temp/RtmpIHbeKi/history.odc)
command #Bugs:history cannot be executed (is greyed out)
save(C:/Users/www/AppData/Local/Temp/RtmpIHbeKi/log.odc)
save(C:/Users/www/AppData/Local/Temp/RtmpIHbeKi/log.txt)

用的你给的数据，代码，我还用了最新的R，弄了半天也没做出来，还是那个结果
Error in bugs.run(n.burnin, bugs.directory, WINE = WINE, useWINE = useWINE,  :
  Look at the log file and
try again with 'debug=TRUE' to figure out what went wrong within Bugs.

没有问题的，你在仔细核查下吧

一直在试着，怎么会这样，难道是我的winbug有问题：
Error in bugs.run(n.burnin, bugs.directory, WINE = WINE, useWINE = useWINE,  :
  Look at the log file and
try again with 'debug=TRUE' to figure out what went wrong within Bugs.

您还能知道是什么原因吗？ 是Winbug不能用了是吗？

我也是遇到了相关的问题，学习了~