RMS calculation in the Diagnostics

If you are use to work with ISI Scan, you can see a noise statistic summary for every cycle (totally 10) of the noise spectra, and two of those statistics are the bias and RMS.

Here I show the statistics for the first four cycles, but we only consider the NIR segment:

 

This post checks how ISI Scan calculates the RMS, and we can see that this RMS value is the RMS corrected by the Bias, so it tells us a measure of the random noise.

I show a simple script showing this:

cycle1<-noise[1,]
cycle2<-noise[2,]
cycle3<-noise[3,]
cycle4<-noise[4,]

 

options(digits=2)
rms1<-sqrt(mean((cycle1)^2)-(rowMeans(cycle1))^2)
rms2<-sqrt(mean((cycle2)^2)-(rowMeans(cycle2))^2)
rms3<-sqrt(mean((cycle3)^2)-(rowMeans(cycle3))^2)
rms4<-sqrt(mean((cycle4)^2)-(rowMeans(cycle4))^2)

> rms1
    1 
0.014 
> rms2
    2 
0.016 
> rms3
    3 
0.011 
> rms4
    4 
0.015