# NOT RUN {
data(elowitz_data)
# Normalize data such that they are
# comparable to Fig 3a in Elowitz et al. (2002).
# Normalized data have mean 1.
D22.cfp.norm <- (elowitz_data$D22[,1]-mean (elowitz_data$D22[,1]))/sd(elowitz_data$D22[,1])/8+1
D22.yfp.norm <- (elowitz_data$D22[,2]-mean (elowitz_data$D22[,2]))/sd(elowitz_data$D22[,2])/8+1
M22.cfp.norm <- (elowitz_data$M22[,1]-mean (elowitz_data$M22[,1]))/sd(elowitz_data$M22[,1])/12+1
M22.yfp.norm <- (elowitz_data$M22[,2]-mean (elowitz_data$M22[,2]))/sd(elowitz_data$M22[,2])/12+1
# Compute noise estimates.
# Since the mean is 1, estimates with and without
# the scaling are the same.
unlist (computeIntrinsicNoise (D22.cfp.norm, D22.yfp.norm))
unlist (computeExtrinsicNoise (D22.cfp.norm, D22.yfp.norm))
unlist (computeIntrinsicNoise (M22.cfp.norm, M22.yfp.norm))
unlist (computeExtrinsicNoise (M22.cfp.norm, M22.yfp.norm))
# }
Run the code above in your browser using DataLab