Convert Fit to Data Frame — param_list_to_peak

Convert a parameter list into a peak data frame

Usage

param_list_to_peak_df(param_list, spec_names = NULL)

Arguments

param_list: list of fit parameters (or a list of such lists)
spec_names: character vector of spectrum names

Value

A data frame with the following columns:

peak: peak number
fit: fit cluster number, with all peaks in the same cluster having the same r2, scalar couplings, and m0
f_pvalue: optional, p-value determined from F-test during iterative fitting
omega0_ppm_1: chemical shift of singlet/doublet center in the first dimension (ppm)
omega0_ppm_2: chemical shift of singlet/doublet center in the second dimension (ppm)
sc_hz_1: optional, scalar coupling of doublet in first dimension (Hz)
sc_hz_2: optional, scalar coupling of doublet in second dimension (Hz)
r2_hz_1: R2 in first dimension (Hz)
r2_hz_2: R2 in first dimension (Hz)
...: m0 values for each spectrum

Details

This function takes the input or (if present) output parameters from a fit and converts them into a data frame. A parameter list must contain three lists:

start_list or fit_list: input or output values of the respective fit parameters
group_list: group numbers for the fit parameters
comb_list: coefficients for deriving fit parameters from a linear combination other auxiliary parameters

This function currently assumes the fit parameters were generated by fit_peak_iter, fit_peak_cluster, or peak_df_to_param_list. These functions use a particular convention for group_list and comb_list to represent either singlets or doublets in each dimension of a 2D spectrum.

This function can take either a single parameter list or a list of parameter lists. If the latter is given, then the results from all the parameter lists will be combined into a single table.

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
param_list_to_peak_df(peak_fits)
#>   peak fit     f_pvalue omega0_ppm_1 omega0_ppm_2  sc_hz_1  r2_hz_1  r2_hz_2
#> 1    1   1 4.566421e-10     8.247602     121.8666 3.280589 2.907218 2.334497
#> 2    2   1 1.118991e-05     8.259565     121.9299 3.280589 2.907218 2.334497
#> 3    3   2 1.876528e-15     8.540030     119.7611 2.000000 4.788566 2.099646
#> 4    4   2 5.817124e-04     8.520232     119.7266 2.000000 4.788566 2.099646
#> 5    5   3 7.526979e-13     8.612449     123.3640 7.648082 5.284891 2.180120
#> 6    6   3 2.592298e-27     8.585348     123.0315 7.648082 5.284891 2.180120
#> 7    7   3 1.342406e-05     8.644864     123.4689 7.648082 5.284891 2.180120
#> 8    8   3 1.467971e-13     8.536996     122.9636 7.648082 5.284891 2.180120
#>        1.ft2
#> 1  824420657
#> 2  240560662
#> 3 1020008726
#> 4   89977216
#> 5  848579189
#> 6  607904936
#> 7  147984411
#> 8  161971930