R stuff for normal distribution fitting

Look at this site for more: http://www.bigre.ulb.ac.be/Users/jvanheld/statistics_bioinformatics/practicals/microarray_fitting_solutions.html

   1:   

   2:  # data loading

   3:  filepath <- system.file("data", "morley.tab" , package="datasets")

   4:  mm <- read.table(filepath)

   5:  m <- mm[,1]

   6:   

   7:  hist(m)

   8:   

   9:  ## install

  10:  library(fBasics)

  11:   

  12:  skewness(m)

  13:   

  14:  kurtosis(m)

  15:   

  16:  plot(density(m))

  17:   

  18:  plot(ecdf(m))

  19:   

  20:  qqnorm(m)

  21:  abline(0,1)

  22:   

  23:  gal <- m

  24:   

  25:  ## Calculate estimators

  26:  m <- mean(gal,na.rm=T)

  27:  s <- sd(gal,na.rm=T)

  28:   

  29:  ## Draw the density histogram of the galactose microarray values

  30:  h <- hist(gal,breaks=100,col='#CCCCFF',border='#CCCCFF',freq=F)

  31:   

  32:  ## On the histogram, draw vertical bars at the following values :

  33:  ## mean, mean + 1*sd, mean -1*sd, mean +2*sd, mean -2*sd

  34:  abline(v=c(m,m-s,m-2*s,m+s,m+2*s),col="#000088",lwd=1)

  35:   

  36:  ## Superimpose the theoretical distribution

  37:  lines(h$mids,dnorm(h$mids,m,s), type="l", lwd=2,col="red")