r - 为什么与 rjags 和 R2Jags 拟合的模型输出不同?
问题描述
我正在使用组级预测器拟合多级逻辑回归模型。我通过 R 使用 JAGS。当我将模型与包相匹配时,我会得到不同的runjags
行为R2Jags
。
我试图编写一个可重现的示例来显示该问题。下面,我模拟来自二项式模型的数据,将数据索引到 8 个图和 2 个块,然后拟合多级逻辑回归以恢复下面代码中的成功概率 (b1
和b2
)。滚动到底部以查看两个配合的摘要。
我的问题是:
- 为什么这两个拟合的后验不同?我使用相同的数据,单个模型规范,并在每个之前设置随机数生成器。为什么后验的平均值不同,为什么Rhat值如此不同?
# -------------------------------------------------------------------
# Loading required packages
# -------------------------------------------------------------------
library(rjags)
library(R2jags)
library(MCMCvis)
包版本信息:
jags.version()
[1] ‘4.3.0’
R2jags_0.5-7 MCMCvis_0.13.5 rjags_4-10
# -------------------------------------------------------------------
# Simulate data
# -------------------------------------------------------------------
set.seed(10)
N.plots = 8
N.blocks = 2
trials=400
n = rep(100,trials)
N=length(n)
plotReps=N/N.plots
blockReps=N/N.blocks
# Block 1
b1<-rep(c(.25,.75,.9,.1),each=plotReps)-.05
# Block 2
b2<-rep(c(.25,.75,.9,.1),each=plotReps)+.05
y = rbinom(trials, 100, p = c(b1,b2))
# vectors indexing plots and blocks
plot = rep(1:8,each=plotReps)
block = rep(1:2,each=blockReps)
# pass data to list for JAGS
data = list(
y = y,
n = n,
N = length(n),
plot = plot,
block= block,
N.plots = N.plots,
N.blocks = N.blocks
)
# -------------------------------------------------------------------
# Code for JAGS model
# -------------------------------------------------------------------
modelString <- "model {
## Priors
# hyperpriors
mu.alpha ~ dnorm(0, 0.0001)
sigma.plot ~ dunif(0,100)
tau.plot <- 1 / sigma.plot^2
sigma.block ~ dunif(0,100)
tau.block <- 1 / sigma.block^2
# priors
for(i in 1:N.plots){
eps.plot[i]~dnorm(0,tau.plot)
}
for(i in 1:N.blocks){
eps.block[i]~dnorm(0,tau.block)
}
# Likelihood
for(i in 1:N){
logit(p[i]) <- mu.alpha + eps.plot[plot[i]] + eps.block[block[i]]
y[i] ~ dbin(p[i], n[i])
}
}"
# -------------------------------------------------------------------
# Initial values
# -------------------------------------------------------------------
# set inits for rjags
inits = list(list(mu.alpha = 0,sigma.plot=2,sigma.block=2),
list(mu.alpha = 0,sigma.plot=2,sigma.block=2),
list(mu.alpha = 0,sigma.plot=2,sigma.block=2))
# set inits function for R2jags
initsFun<-function(){list(
mu.alpha=0,
sigma.plot=2,
sigma.block=2
)}
# -------------------------------------------------------------------
# Set JAGS parameters and random seed
# -------------------------------------------------------------------
# scalars that specify the
# number of iterations in the chain for adaptation
# number of iterations for burn-in
# number of samples in the final chain
n.adapt = 500
n.update = 5000
n.iterations = 1000
n.thin = 1
parsToMonitor = c("mu.alpha","sigma.plot","sigma.block","eps.plot","eps.block")
# -------------------------------------------------------------------
# Call to JAGS via rjags
# -------------------------------------------------------------------
set.seed(2)
# tuning (n.adapt)
jm = jags.model(textConnection(modelString), data = data, inits = inits,
n.chains = length(inits), n.adapt = n.adapt)
# burn-in (n.update)
update(jm, n.iterations = n.update)
# chain (n.iter)
samples.rjags = coda.samples(jm, variable.names = c(parsToMonitor), n.iter = n.iterations, thin = n.thin)
# -------------------------------------------------------------------
# Call to JAGS via R2jags
# -------------------------------------------------------------------
set.seed(2)
samples.R2jags <-jags(data=data,inits=initsFun,parameters.to.save=parsToMonitor,model.file=textConnection(modelString),
n.thin=n.thin,n.chains=length(inits),n.burnin=n.adapt,n.iter=n.iterations,DIC=T)
# -------------------------------------------------------------------
# Summarize posteriors using MCMCvis
# -------------------------------------------------------------------
sum.rjags <- MCMCvis::MCMCsummary(samples.rjags,params=c("mu.alpha","eps.plot","sigma.plot","sigma.block","eps.block"))
sum.rjags
sum.R2jags2 <- MCMCvis::MCMCsummary(samples.R2jags,params=c("mu.alpha","eps.plot","sigma.plot","sigma.block","eps.block"))
sum.R2jags2
这是 rjags 拟合的输出:
mean sd 2.5% 50% 97.5% Rhat n.eff
mu.alpha 0.07858079 21.2186737 -48.99286669 -0.04046538 45.16440893 1.11 4063
eps.plot[1] -1.77570813 0.8605892 -3.45736942 -1.77762035 -0.02258692 1.00 2857
eps.plot[2] -0.37359614 0.8614370 -2.07913650 -0.37581522 1.36611635 1.00 2846
eps.plot[3] 0.43387001 0.8612820 -1.24273657 0.42332033 2.20253810 1.00 2833
eps.plot[4] 1.31279883 0.8615840 -0.38750596 1.31179143 3.06307745 1.00 2673
eps.plot[5] -1.34317034 0.8749558 -3.06843578 -1.34747145 0.44451006 1.00 2664
eps.plot[6] -0.40064738 0.8749104 -2.13233876 -0.41530587 1.37910977 1.00 2677
eps.plot[7] 0.36515253 0.8738092 -1.35364716 0.35784379 2.15597251 1.00 2692
eps.plot[8] 1.71826293 0.8765952 -0.01057452 1.70627507 3.50314147 1.00 2650
sigma.plot 1.67540914 0.6244529 0.88895789 1.53080631 3.27418094 1.01 741
sigma.block 19.54287007 26.1348353 0.14556791 6.68959552 93.21927035 1.22 94
eps.block[1] -0.55924545 21.2126905 -46.34099332 -0.24261169 48.81435107 1.11 4009
eps.block[2] 0.35658731 21.2177540 -44.65998407 0.25801739 49.31921639 1.11 4457
这是 R2jags fit 的输出:
mean sd 2.5% 50% 97.5% Rhat n.eff
mu.alpha -0.09358847 19.9972601 -45.81215297 -0.03905447 47.32288503 1.04 1785
eps.plot[1] -1.70448172 0.8954054 -3.41749845 -1.70817566 0.08187877 1.00 1141
eps.plot[2] -0.30070570 0.8940527 -2.01982416 -0.30458798 1.46954632 1.00 1125
eps.plot[3] 0.50295713 0.8932038 -1.20985348 0.50458106 2.29271214 1.01 1156
eps.plot[4] 1.37862742 0.8950657 -0.34965321 1.37627777 3.19545411 1.01 1142
eps.plot[5] -1.40421696 0.8496819 -3.10743244 -1.41880218 0.25843323 1.01 1400
eps.plot[6] -0.45810643 0.8504694 -2.16755579 -0.47087931 1.20827684 1.01 1406
eps.plot[7] 0.30319019 0.8492508 -1.39045509 0.28668886 1.96325582 1.01 1500
eps.plot[8] 1.65474420 0.8500635 -0.03632306 1.63399429 3.29585024 1.01 1395
sigma.plot 1.66375532 0.6681285 0.88231891 1.49564854 3.45544415 1.04 304
sigma.block 20.64694333 23.0418085 0.41071589 11.10308188 85.56459886 1.09 78
eps.block[1] -0.45810120 19.9981027 -46.85060339 -0.33090743 46.27709625 1.04 1795
eps.block[2] 0.58896195 19.9552211 -46.39310677 0.28183123 46.57874408 1.04 1769
这是来自 2 次拟合的 mu.alpha 的跟踪图。首先,从 rjags 适合:
二、来自R2jags的合身:
解决方案
我很确定你的后验不同的原因是因为 Jags 不关心 R 代码中的种子集。
然而!虽然set.seed()
rjags 直接对 Jags 没有任何作用,并且在通过 rjags 直接调用 Jags 时也没用,但当您使用 R2Jags 时它确实会传播。
让我们比较一下:
- rjags 是一个较低级别的接口。如果您不提供随机生成器的选择,那么
inits
Jags 中的种子将基于它们的初始化基于当前时间戳。 - R2Jags 包装了 rjags 函数。(
jags()
R2Jags) 函数调用jags.model()
(rjags)。如果您查看jags()
函数的 R 代码,您将看到它使用 R 中的runif()
函数为每个链生成一个种子。由于 Jags 种子依赖于 R 中runif()
函数的输出,因此在 R 中设置种子将确保每次跑步时,您都会获得相同的 Jags 种子。
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