norm-gaussian.r

normalize <- function (m) {
  result <- m
  for (r in 1:dim (m)[1]) {
    avg <- mean (m [r,], na.rm=TRUE)
    variance <- var (m [r,], na.rm=TRUE)
    result [r,] <- result [r,] - avg
    result [r,] <- result [r,] / sqrt (variance)
  }
  return (result)
}

main <- function(normalizep) {
  data <- read.table("data.res", header=TRUE)
  x <- data$x
  if (normalizep)
    data.norm <- normalize (t(data[2:dim(data)[2]]))
  else
    data.norm <- t(data[2:dim(data)[2]])
  iter.num <- c (1:dim(data.norm)[1])
  iter.pch <- c ('x', 'o', '^', '*', '~')
  matplot(x, t(data.norm), col=iter.num, pch=iter.pch, xlab="Scattering angle (°)", ylab="Scatter intensity (µV) (normalized)")
  for (i in iter.num) {
    y <- data.norm[i,]
    y.gauss <- glm.fit(x, y, family=gaussian())
    lines(spline(x, y.gauss$y), col=i)
  }
}

main(1)