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Iterative Peak Fitting

Source: R/fitnmr.R
fit_peak_iter.Rd

Iteratively fit peaks for whole spectra

Usage

fit_peak_iter(
  spectra,
  noise_sigma = NULL,
  noise_cutoff = 15,
  f_alpha = 0.001,
  iter_max = 100,
  omega0_plus = c(0.075, 0.75),
  r2_start = 5,
  r2_bounds = c(0.5, 20),
  sc_start = c(6, NA),
  sc_bounds = c(2, 12),
  fit_list = list(),
  plot_fit = FALSE,
  plot_fit_stages = FALSE,
  iter_callback = NULL,
  verbose = TRUE
)

Arguments

spectra

list of spectrum objects read by read_nmrpipe.

noise_sigma

numeric vector of noise levels associated with each spectrum. If NULL, it is calculated with noise_estimate.

noise_cutoff

numeric value multiplied by noise_sigma to determine cutoffs for each spectrum. Peak fitting will terminate if the maximum residuals for all spectra fall below these cutoffs.

f_alpha

numeric value giving a F-test p-value threshold above which a peak will not be accepted.

iter_max

integer maximum number of iterations to apply.

omega0_plus

numeric vector giving the window size (ppm plus or minus the starting omega0 values) around which to use points from the spectra for fitting.

r2_start

numeric vector giving the starting r2 value(s) for the fit (in Hz).

r2_bounds

numeric vector of length two giving the lower and upper bounds for r2.

sc_start

numeric vector giving the starting scalar coupling values for doublets. It should be the same length as the number of dimensions in the spectrum. Set the value to NA for a given dimension to make it a singlet.

sc_bounds

numeric vector of length two giving the lower and upper bounds for scalar couplings.

fit_list

list of previous fits to which the new fits should be appended.

plot_fit

logical indicating whether produce a fit cluster plot for each iteration.

plot_fit_stages

logical indicating whether to plot each stage of fitting within the iterations.

iter_callback

function called after each iteration with two arguments: iter and iter_max

verbose

logical indicating whether to print progress updates

Value

List of fit objects returned by fit_peak_cluster, one for each iteration. They are appended to fit_list if supplied.

Details

This function uses an iterative algorithm to fit all the peaks in a given list of spectra, assuming identical peak positions and shapes across the spectra. Each iteration starts by identifying the maximum value across all spectra, using that position to fit a cluster of overlapping peaks with the fit_peak_cluster function. After the cluster of peaks is fit, the modeled intensity is subtracted from all spectra and another iteration is performed. Iterations are terminated if max_iter is reached or the residual intensity in all spectra falls below noise_sigma*noise_cutoff.

This function currently only supports fitting of 2D spectra, but will be generalized to work with spectra of any dimensionality in the near future. To reduce the number of false positives/negatives, the most important parameters to adjust are noise_cutoff, f_alpha, and iter_max. If iter_max is reached before all peaks have been identified, then you can call this function again, setting the fit_list parameter to the return value of the previous invocation. In that case, iter_max new iterations will be performed and appended to fit_list.

For visualizing the iterative algorithm as it progresses, you can enable either the plot_fit or plot_fit_stages parameters.

Examples

spec_file <- system.file("extdata", "t1", "1.ft2", package = "fitnmr")
spec <- list("1.ft2" = read_nmrpipe(spec_file, dim_order = "hx"))
peak_fits <- fit_peak_iter(spec, iter_max = 3)
#> Fit iteration 1:
#>   0 ->  6 fit parameters: F = 333.8 (p = 4.56642e-10)
#>   6 ->  9 fit parameters: F = 64.6 (p = 1.11899e-05)
#> Warning: lmder: info = -1. Number of iterations has reached `maxiter' == 200.
#>   9 -> 12 fit parameters: F = 1.2 (p = 0.464698)
#>  Terminating search because F-test p-value > 0.001
#> Fit iteration 2:
#>   0 ->  6 fit parameters: F = 734.0 (p = 1.87653e-15)
#>   6 ->  9 fit parameters: F = 14.7 (p = 0.000581712)
#>   9 -> 12 fit parameters: F = 0.1 (p = 0.976358)
#>  Terminating search because F-test p-value > 0.001
#> Fit iteration 3:
#>   0 ->  6 fit parameters: F = 27.7 (p = 7.52698e-13)
#>   6 ->  9 fit parameters: F = 226.0 (p = 2.5923e-27)
#>   9 -> 12 fit parameters: F = 13.3 (p = 1.34241e-05)
#>  12 -> 15 fit parameters: F = 68.7 (p = 1.46797e-13)
#>  15 -> 18 fit parameters: F = 7.0 (p = 0.00144316)
#>  Terminating search because F-test p-value > 0.001