we don’t actually ever calculate the height errors at he moment (although our data model has space for this - hence there is a column in the tables).
When we do a fit, it is a simple Gaussian fit and we don’t do any error analysis (it would mean implementing a Monte Carlo or similar type of process to do this). The best way to get an estimate of your errors would be to take a look at your spectrum noise levels.
As it happens, I’m in the process of writing some documentation on this at the moment. I’ve copied the section on spectrum noise levels below. Hopefully that’ll help.
Spectrum Noise Levels
Each spectrum has a noiseLevel (and a corresponding negativeNoiseLevel). When you first load a spectrum, we do a quick estimation of the noiseLevel which is used to set the initial contour level (contourBase) of your spectrum. Generally the contourBase will be set to 1.41 times the noiseLevel. This allows fast loading of spectra and ensures your spectrum has a reasonable initial contour level when you first open it in a SpectrumDisplay. A full noise estimation would slow things down.
If you want more accurate information about the noise in your spectrum, you can right-click on your SpectrumDisplay and select Estimate Noise (or use the shortcut EN). This will bring up the Estimate Noise pop-up which allows you to estimate the noise for all the spectra in the active SpectrumDisplay. You can estimate the noise either using the Visible Area or Random Sampling methods. You will get back information about the mean, standard deviation and min/max points of the noise sample used. The Estimated noise level is set at 3.5 times the standard deviation of the noise. Assuming a Gaussian/Normal distribution of the noise, this should set your noiseLevel just above (or pretty much at the maximum value of) your noise.
The pop-up allows you to re-estimate the noise if you wish and to set the spectrum noiseLevel to the Estimated noise level.
Visible Area Method
This method will take all the points of the spectrum which are currently shown in the active SpectrumDisplay and use these to estimate the noise. This method assumes that the region shown only contains noise, so you first have to zoom into a part of the spectrum without any peaks before you bring up the Estimate Noise pop-up.
Random Sampling Method
The Random Sampling method will select a number of points in your spectrum to use for the noise estimation. Typically, a minimum of 3% of points per axis up to a maximum of 10,000 points are used. Of these points, lots of random subsets are chosen and used to estimate the mean and standard deviation of the noise. The subset with the lowest standard deviation is the final one used. This ensures that you are less likely to have accidentally included a real signal in your random sample.