Text string in Knitr (Rstudio): Error in parse - r

Question

I am attempting to use knitr trough Rstudio to document a model that save a text string to a *txt file.
When doing so, i get this R markdown error message:
*Error in parse(text = x, srcfile = src) : <text>:2:24: unexpected
INCOMPLETE_STRING 14: var.m <- 1/tau.m # between-trial variance 15:
Calls: <Anonymous> ... <Anonymous> -> parse_all -> parse_all.character -> parse*
Anyone know to fix this?
This string works fine:
Modelstring.baseline = " Text goes here "
This string works fine:
Modelstring.baseline = "
# Binomial likelihood, logit link, MTC
# Fixed effect model
#CV mortality
model{ # *** PROGRAM STARTS
for(i in 1:ns){ # LOOP THROUGH STUDIES
mu[i] ~ dnorm(0,.0001) # vague priors for all trial baselines
for (k in 1:na[i]) { # LOOP THROUGH ARMS
r[i,k] ~ dbin(p[i,k],n[i,k]) # binomial likelihood
logit(p[i,k]) <- mu[i] + d[t[i,k]]-d[t[i,1]] # model for linear predictor
rhat[i,k] <- p[i,k] * n[i,k] # expected value of the numerators
dev[i,k] <- 2 * (r[i,k] * (log(r[i,k])-log(rhat[i,k])) # Deviance contribution
+ (n[i,k]-r[i,k]) * (log(n[i,k]-r[i,k]) - log(n[i,k]-rhat[i,k])))
}
resdev[i] <- sum(dev[i,1:na[i]]) # summed residual deviance contribution for this trial
}
totresdev <- sum(resdev[]) # Total Residual Deviance
d[1]<- 0 # treatment effect is zero for reference treatment
for (k in 2:nt) { d[k] ~ dnorm(0,.0001) } # vague priors for treatment effects
"
Whiles this string generate a parser error:
Modelstring.baseline = "
model{ # *** PROGRAM STARTS
for (i in 1:ns)
{ # LOOP THROUGH STUDIES
r[i] ~ dbin(p[i],n[i]) # Likelihood
logit(p[i]) <- mu[i] # Log-odds of response
mu[i] ~ dnorm(m,tau.m) # Random effects model
}
mu.new ~ dnorm(m,tau.m) # predictive dist. (log-odds)
m ~ dnorm(0,.0001) # vague prior for mean
var.m <- 1/tau.m # between-trial variance
#---Non-informative prior
#tau.m <- pow(sd.m,-2)
#sd.m ~ dunif(0,5)
#---Vaguely informative prior
#tau.m ~ dgamma(0.001,.001)
#sd.m ~ pow(tau.m,-0.5)
#---Informative prior R.M Turner et al LN(-3.95, 1.79)
tau.m <- 1/tausq
tausq ~ dlnorm(-3.95, 0.31) #0.31 = 1/(1.79*1.79)
}
"