如何在Sweave中編譯Latex文檔而不必每次都運行rjags？

我正在編寫pdf，使用sweave和latex在r。對於MCMC方法，我正在運行rjags。運行這些模型大約需要一個小時才能收斂。每次運行我的sweave代碼編譯PDF時，它也會再次運行所有jags型號。這使編輯和發現，如果我需要花費一個小時來編譯PDF，那麼是否會造成小的語法錯誤。我如何保留我的jags代碼生成的所有變量，但不必每次都要評估sweave？數據可以在這裏找到：https://uwyo-files.instructure.com/courses/481850/files/36253354/course%20files/project4_genomebinom/chrgc.txt?download=1&inline=1&sf_verifier=ea3569eec1ca938fad4122a92e35ff57&ts=1462863980&user_id=569842 如何在Sweave中編譯Latex文檔而不必每次都運行rjags？

下面是一些示例代碼

\documentclass[12pt, letterpaper]{article} 
\begin{document} 

<<computation,results=hide>>= 
humangc <- read.csv("c:\\temp\\RtmpYpMfSP\\data15a4519241c1") 
chr<-substr(humangc$chr, 4, 8) 
chr[chr=='X']<-23 
chr[chr=='Y']<-24 
chr<-as.numeric(chr) 
humangc<-data.frame(humangc[,-1], chr=chr) 
humangc<-humangc[order(humangc$chr, humangc$bp),] ### reorder data by chr 

## drop NA data and blocks with fewer than 100000 (10%) valid data 
humangc<-humangc[!is.na(humangc$valid) & humangc$valid > 100000,] 


### hierarchical Bayesian model in JAGS 
bin.beta.beta<-" 
model{ 
for(i in 1:bins){ 
gc[i] ~ dbinom(p[i], n[i]) 
p[i] ~ dbeta(chrgc * chrprec, (1-chrgc)*chrprec) 
} 

chrgc ~ dbeta(1,1) ## chrgc is same as pi 
chrprec ~ dunif(0.001,10000) ## chrprec is same as theta 
} 
" 
require(rjags) 

for(i in 1:24){ 
data.jags<-list(gc=humangc$gc[humangc$chr==i], 
       n=humangc$valid[humangc$chr==i], 
       bins=length(humangc$gc[humangc$chr==i])) 

mod.jags<- jags.model(textConnection(bin.beta.beta),data=data.jags,n.chains=3,n.adapt=1000) 

mod.samples<-jags.samples(model=mod.jags, variable.names=c("chrgc", "chrprec"), n.iter=5000,thin=2) 
### summarize quantiles of beta and p-values of empirical obs from Beta 

gcest<-NULL 

gcest$q<-qbeta(c(0.025, 0.5, 0.975), 
       mean(mod.samples$chrprec[1,,] * mod.samples$chrgc[1,,]), 
       mean(mod.samples$chrprec[1,,] * (1-mod.samples$chrgc[1,,]))) 
gcest$p<-pbeta(humangc$perc[humangc$chr==i], 
       mean(mod.samples$chrprec[1,,] * mod.samples$chrgc[1,,]), 
       mean(mod.samples$chrprec[1,,] * (1-mod.samples$chrgc[1,,]))) 
gcest$perc <- humangc$perc[humangc$chr==i] 
gcest$bp <- humangc$bp[humangc$chr==i] 
## write workspace for chromosome to disk 
save.image(paste("Rworkspace_chr", i, sep="")) 
    } 
@ 


<<echo=F, fig=T, include=F>>= 
update(mod.jags) 
require(coda) 
params <- c("chrgc", "chrprec") 
samps <- coda.samples(mod.jags, params, n.iter = 2000) 
plot(samps) 
@ 
SOME TRIVIAL TEXT!!!!!! 
end{document}

正如你可以看到，我創造24個不同的模型。這需要一點時間。我如何得到「一些有趣的文字!!!!!!!」當我編譯pdf時顯示得很快，因爲我需要由jags創建的變量？