Error running /usr/bin/pdflatex (exit code 70) - r

Question

my code for gibbs sampling is like this:
I cannot compile it. The error message is Error running /usr/bin/pdflatex (exit code 70).
I don't see any font problem in my code. Can anyone help me to fix it? Thanks!
Why?
\documentclass{article}
\usepackage{float}
\begin{document}
\SweaveOpts{concordance=TRUE}
\title{Lab 8 for STA 601}
\author{Siyang Li}
\date{2014/11/4}
\maketitle
\section{part 1}
see attached file
\section{part 2}
<<gibbssampling, echo = TRUE, fig = TRUE, include = TRUE>>=
library(msm)
library(coda)
## read in data, data is on a log scale
data <- read.table("data.txt", header = TRUE)
data1 <- data[data$day == "Saturday"|data$day == "Sunday",] ##data for weekday
data2 <- data[data$day == "Monday"|data$day == "Tuesday"|data$day == "Wednesday"|data$day == "Thursday"|data$day == "Friday",] ##data for weekend
dev.off()
S <- 20000
## to save the results
mu1.draws <- matrix(NA, nrow = S, ncol = 1)
mu2.draws <- matrix(NA, nrow = S, ncol = 1)
phi1.draws <- matrix(NA, nrow = S, ncol = 1)
phi2.draws <- matrix(NA, nrow = S, ncol = 1)
## starting points
mu1.draws[1,1] <- mu1.cur <- 1
mu2.draws[1,1] <- mu2.cur <- 1
phi1.draws[1,1] <- phi1.cur <- 0.01
phi2.draws[1,1] <- phi2.cur <- 0.1
##prior specification
m1 <- 0
v1 <- 0.01
m2 <- 0
v2 <- 0.02
alpha1 <- 0.1
beta1 <- 0.1
alpha2 <- 0.2
beta2 <- 0.2
n1 <- length(data1)
n2 <- length(data2)
##gibbs sampling
for(i in 2:S){
##update mu1
sigma1.post <- 1/(n1*phi1.cur + 1/v1)
mu1.post <- (sum(data1$pm)*phi1.cur + m1/v1)*sigma1.post
mu1.cur <- rtnorm(1,mu1.post, sqrt(sigma1.post), mu2.cur,Inf)
##update phi1
alpha1.post <- alpha1 + n1/2
beta1.post <- beta1 + 1/2*sum((data1$pm - mu1.cur)^2)
phi1.cur <- rgamma(1,alpha1.post, beta1.post)
##update mu2
sigma2.post <- 1/(n2*phi2.cur + 1/v2)
mu2.post <- (sum(data2$pm)*phi2.cur + m2/v2)*sigma2.post
mu2.cur <- rtnorm(1,mu2.post,sqrt(sigma2.post),-Inf,mu1.cur)
##update phi2
alpha2.post <- alpha2 + n2/2
beta2.post <- beta2 +1/2*sum((data2$pm - mu2.cur)^2)
phi2.cur <- rgamma(1,alpha2.post, beta2.post)
##save the results
mu1.draws[i,1] <- mu1.cur
mu2.draws[i,1] <- mu2.cur
phi1.draws[i,1] <- phi1.cur
phi2.draws[i,1] <- phi2.cur
}
Burnin <- 5000
##convergence diagonostics
mu.mcmc <- mcmc(mu1.draws)
plot(mu.mcmc)
autocorr.plot(mu.mcmc)
mean(mu1.draws[(Burnin+1):S,1])
mean(mu2.draws[(Burnin+1):S,1])
plot(mu1.draws[(Burnin+1):S,1])
plot(phi1.draws[(Burnin+1):S,1])
#
\section{part 3}
<<intervals,echo = TRUE, fig = TRUE, include = TRUE>>=
cat("mean value of mu1 is",mean(mu1.draws[(Burnin+1):S]),"\n")
cat("95% interval of mu1 is",quantile(mu1.draws[(Burnin+1):S],c(0.025,0.975)),"\n")
cat("mean value of mu2 is",mean(mu2.draws[(Burnin+1):S]),"\n")
cat("95% interval of mu2 is",quantile(mu2.draws[(Burnin+1):S],c(0.025,0.975)),"\n")
cat("mean value of phi1 is",mean(phi1.draws[(Burnin+1):S]),"\n")
cat("95% interval of phi1 is",quantile(phi1.draws[(Burnin+1):S],c(0.025,0.975)),"\n")
cat("mean value of phi2 is",mean(phi2.draws[(Burnin+1):S]),"\n")
cat("95% interval of phi2 is",quantile(phi2.draws[(Burnin+1):S],c(0.025,0.975)),"\n")
#
\section{part 4}
<<posteriorprob, echo = TRUE, fig = TRUE, include = TRUE>>=
mean(mu1.draws[(Burnin+1):S] > mu2.draws[(Burnin+1):S])
mean(phi1.draws[(Burnin+1):S] < phi2.draws[(Burnin+1):S])
#
\section{part 5}
<<prediction, echo = TRUE, fig = TRUE, include = TRUE>>=
for(i in (Burnin+1):S){
mu1 <- mu1.draws[i]
phi1 <- phi1.draws[i]
mu2 <- mu2.draws[i]
phi2 <- phi2.draws[i]
y1 <- rnorm(1,mu1,sqrt(1/phi1))
y2 <- rnorm(1,mu2,sqrt(1/phi2))
}
mean(y1 > y2)
#
\end{document}