#data=read.delim("F:/promec/Gygi/ftp.pride.ebi.ac.uk/pride//data//archive//2015//06//PXD001468/b1906_293T_proteinID_01A_QE3_122212_et.txt", skip = 1, header=T)
data=read.delim("F:/promec/Elite/LARS/2015//desember/PD21/130814_frak5_monoQ_tod27/Set of 5 files_PSMsR.txt",header=T)

#dhist=hist((data$exp_neutral_mass-data$calc_neutral_mass)/data$charge,breaks=50000,freq=F )
dhist=hist(data$Delta.mz.in.Da,breaks=100,freq=F)

warnings()

## NULL

hist(log10(dhist$density))

max(log10(dhist$density))

## [1] -2.299289

#min(abs(dhist$breaks[dhist$density>0.01]))
#plot(dhist$breaks[dhist$density>0.01])

#https://cran.r-project.org/web/packages/mclust/vignettes/mclust.html
library(mclust)

## Warning: package 'mclust' was built under R version 3.2.5

## Package 'mclust' version 5.2

## Type 'citation("mclust")' for citing this R package in publications.

#mod4 = densityMclust((data$exp_neutral_mass-data$calc_neutral_mass)/data$charge)
dmod4=(data$exp_neutral_mass-data$calc_neutral_mass)/data$charge
mod4 = densityMclust(data$Delta.mz.in.Da)
summary(mod4)

## -------------------------------------------------------
## Density estimation via Gaussian finite mixture modeling 
## -------------------------------------------------------
## 
## Mclust V (univariate, unequal variance) model with 7 components:
## 
##  log.likelihood   n df       BIC       ICL
##       -6043.631 996 20 -12225.34 -12468.85
## 
## Clustering table:
##   1   2   3   4   5   6   7 
##  91 159 151 191 168  70 166

plot(mod4, what = "BIC")

dmod4=data$Delta.mz.in.Da
plot(mod4, what = "density", data = dmod4, breaks = 100)

plot(mod4, what = "diagnostic", type = "qq")

i=0.9
n=1
y1<-0
y2<-0

for(x1 in seq(0.1, 10, 0.5)){
  for(x2 in seq(0.1, 10, 0.5)){
    y1<-c(((1-i)*n+x1)/((1-i)*n+x2),y1)
    y2<-c(x1/x2,y2)
  }
}
plot(log2(y1),log2(y2))

plot((y1),(y2))