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###############################################################################
## R Code to simulate reference data
###############################################################################
## N: number of samples for each of the number of bands
## nrBands: number of bands for which data is simulated
## total number of samples: N*length(nrBands)
## BandCenters: molecular weights are randomly generated around "band centers"
## delta: use uniform distribution with +/- delta around the band centers
## refData: if TRUE also Taxonname and Accession are added
simulateRFLPdata <- function(N = 10, nrBands = 3:12,
bandCenters = seq(100, 800, by = 100),
delta = 50, refData = FALSE){
if(length(N) > 1){
N <- N[1]
warning("Only the first element of 'N' is used.")
}
if(N <= 0) stop("'N' has to be a positive integer!")
N <- trunc(N)
if(any(nrBands <= 0)) stop("'nrBands' has to be a vector of positive integer!")
if(any(bandCenters <= 0)) stop("'bandCenters' has to be a vector of positive reals!")
if(length(delta) > 1){
delta <- delta[1]
warning("Only the first element of 'delta' is used.")
}
if(delta <= 0) stop("'delta' has to be a positive real!")
## data matrix
simData <- matrix(NA, nrow = sum(N*nrBands), ncol = 3)
colnames(simData) <- c("Sample", "Band", "MW")
row.count <- 0
sample.count <- 1
for(i in nrBands){
for(j in 1:N){
## randomly select "band centers" (with replacement!)
Bcent <- sample(bandCenters, i, replace = TRUE)
## simulate molecular weights
simData[row.count+(1:i),] <- c(rep(sample.count, i),
1:i,
sort(runif(i, min = Bcent-delta, max = Bcent+delta)))
row.count <- row.count + i
sample.count <- sample.count + 1
}
}
## Generate data.frame and add column Enzyme
simData <- data.frame(simData, Enzyme = "Enzyme 1")
simData$Sample <- paste("Sample", simData$Sample)
if(refData){
simData$Taxonname <- simData$Sample
simData$Accession <- simData$Sample
}
simData
}
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