Fit lorentzians to each peak to estimate areas

Source: R/area_estimation.R

The different methods are available for benchmarking while developing, we should pick one.

peaklist_fit_lorentzians(
  peak_data,
  nmr_dataset,
  amplitude_method = c("intensity", "2nd_derivative", "intensity_without_baseline"),
  refine_peak_model = c("none", "peak", "2nd_derivative")
)

Arguments

peak_data

The peak data

nmr_dataset

The nmr_dataset object with the data. This function for now assumes nmr_dataset is NOT be baseline corrected

amplitude_method

The method to estimate the amplitude. It may be:

  • "intensity". The amplitude of the peak is proportional to the raw intensity at the apex. This is a bad estimation if the intensity includes a baseline, because the amplitude of the peak will be overestimated

  • "2nd_derivative": The amplitude of the peak is proportional to the second derivative of the raw intensity signal at the apex. This method aims to correct the "intensity" method, since it is expected that the baseline will be mostly removed when considering the 2nd derivative of the spectrum. The 2nd derivative is calculated with a 2nd order Savitzky-Golay filter of 21 points.

  • "intensity_without_baseline": A baseline is estimated on the whole spectra and subtracted from it. Then the peak amplitude is proportional to the corrected intensity at the apex (as in the "intensity" method).

refine_peak_model

Whether a non linear least squares fitting should be used to refine the estimated parameters. It can be:

  • "none": Do not refine using nls.

  • "peak": Use a lorentzian peak model and the baseline corrected spectra.

  • "2nd_derivative":

Value

The given data frame peak_data, with added columns:

  • inflection points,

  • gamma

  • area

  • a norm_rmse fitting error

As well as some attributes

  • "errors": A data frame with any error in the peak fitting

  • "fit_baseline": Whether the method used has any consideration for the baseline of the signal (maybe not very useful attribute)

  • "method_description": A textual description of what we did, to include it in plots

Details

  • gamma is estimated using the inflection points of the signal and fitting them to the lorentzian inflection points

  • $A$ is estimated using the amplitude_method below

  • The peak position ($x_0$) is given in peak_data

Those estimations may be refined with non-linear least squares using refine_peak_model. If the nls does not converge, the initial estimations are kept. Convergence -and other nls errors- are saved for further reference and diagnostic. Use attr(peak_data_fitted, "errors") to retreive the error messages, where peak_data_fitted is assumed to be the output of this function. The refining improves gamma, $A$ and $x_0$.

The baseline estimation (when calculated, see the arguments) is set to Asymmetric Least Squares with lambda = 6, p=0.05, maxit=20 and it is probably not optimal... yet.