Michaelis-Menten fitting by drm library: "Error in parse:" - r

Question

Here is my code:
data <-data.frame(matrix(0,nrow = 9,ncol = 2))
data[,1] <- c(0,15,41,81,146,211,438,958,1733)
data[,2] <-c(0.000000,5.7013061,13.2662515,26.0874534,42.2710547,55.6050052,75.597474,112.6755999,109.45890071)
rownames(data) <- c("E0_TAP","E3_TAP","E4_TAP","E5_TAP","E6_TAP","E7_TAP","E8_TAP","E10_TAP","E12_TAP")
colnames(data) <- c('S','v')
This is the light saturation curve of photosystem II in Chlamydomonas reinhardtii. I would like to find the best fitting for my curve using the Michaelis-Menten distribution model. I tried with the drm() command in this way :
model.drm <- drm (v ~ cluster(S), data = data, fct = MM.2())
When I run this code the calculation of the fitting starts, but it's interrupted by an error that I do not really comprehend:
Error in parse(text = paste(paste(rep("c(", nrep - 1), collapse = ""), :
<text>:2:39: unexpected ')'
1: mu[(1+( 1 * (i - 1))),] %*%
2: mu[( 2 + ( 1 * (i - 1))),drop=FALSE,])
^
In addition: Warning message:
In cbind(mu[, 2:(nclass - 1)], 1) - mu[, seq(nclass - 1)] :
longer object length is not a multiple of shorter object length
Timing stopped at: 0 0 0
Although I will keep trying solve the problem by myself, I would really appreciate if someone could help me fixing it quicker or finding an alternative way to perform the analysis.
Thanks in advance!

Answer 1

Thanks to the help of a friend here follows the answer:
data <-data.frame(matrix(0,nrow = 9,ncol = 2))
data[,1] <- c(0,15,41,81,146,211,438,958,1733)
data[,2] <-c(0.000000,5.7013061,13.2662515,26.0874534,42.2710547,55.6050052,75.597474,112.6755999,109.45890071)
rownames(data) <- c("E0_TAP","E3_TAP","E4_TAP","E5_TAP","E6_TAP","E7_TAP","E8_TAP","E10_TAP","E12_TAP")
colnames(data) <- c('S','v')
data <- t(data) #traspose
data1 <- cbind(data,data) #duplicate
data1 <- cbind(data1,data1) # quadruplicate
data <- as.data.frame(t(data1)) #transpose
model.drm <- drm (v ~ cluster(S), data = data, fct = MM.2()) #fitting analysis
S <- data[,1]
v <- data[,2]
mml <- data.frame(S = seq(0, max(S)+9000, length.out = 200))
mml$v <- predict(model.drm, newdata = mml)
s <- mml[,1]
v <- mml[,2]
plot(s,v)
lines(s,v,lty=2,col="red",lwd=3)
coeff <- as.data.frame(coef(summary(model.drm)))
The issue comes from the dataset itself. To bypass the error, a n-uplication of my data was needed. I assume that it would be even better having more replicas of the experiment instead of cloning the selfsame.
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