Fitting of multiple spectral lines by the same or different lineshape functions with variable parameters.
Usage
multipeak_fit(
x,
peaks,
profiles,
wL = NULL,
wG = NULL,
A = NULL,
wlgth.min = NULL,
wlgth.max = NULL,
id = NULL,
max.iter = 200
)Arguments
- x
A data frame or tibble of spectra, one spectrum per row. Column names are the wavelengths; an optional identifier column can be given with
id.- peaks
A numeric vector of the (approximate) peak center wavelengths.
- profiles
A character vector of the lineshape functions for fitting, one per peak or a single one used for all peaks: "lorentzian", "gaussian", "voigt" (exact) or "pseudo_voigt" (case insensitive).
- wL
A numeric (single value or one per peak) of the Lorentzian full width at half maximum (initial guess)
- wG
A numeric (single value or one per peak) of the Gaussian full width at half maximum (initial guess)
- A
A numeric (single value or one per peak) of the peak area (initial guess)
- wlgth.min
A numeric of the lower bound of the wavelength subset
- wlgth.max
A numeric of the upper bound of the wavelength subset
- id
A character specifying the name of the column holding the spectra id (optional)
- max.iter
A numeric specifying the maximum number of iteration (200 by default)
Value
A tibble with one row per spectrum and the columns id (or
spectrum), data, fit, tidied and augmented (see peak_fit()).
augmented additionally contains one column per peak (.peak_1, ...)
with the contribution of each fitted line.
Details
The function uses minpack.lm::nlsLM, which is based on the Levenberg-Marquardt
algorithm for searching the minimum value of the square of the sum of the residuals.
All peaks are fitted simultaneously with a common baseline offset:
$$y = y_0 + \sum_{i} A_i \cdot f_i(x; x_{c,i}, w_i)$$
The parameters of peak \(i\) are named with the suffix _i
(e.g. xc_1, wG_1, A_1). Initial values that are not supplied are
estimated from the data around each peak center. Each peak center is
constrained to lie within the fitted wavelength range.
Examples
data(forageLIBS)
mean_spectrum <- colMeans(forageLIBS[-(1:14)])
wl <- as.numeric(names(mean_spectrum))
# the overlapping lines of the K I doublet
doublet <- tibble::as_tibble(as.list(mean_spectrum[wl > 404.1 & wl < 405.0]))
res <- multipeak_fit(doublet, peaks = c(404.414, 404.721), profiles = "gaussian")
res$tidied[[1]]
#> # A tibble: 7 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 y0 1597. 92.5 17.3 6.60e- 5
#> 2 xc_1 404. 0.00896 45147. 1.44e-18
#> 3 wG_1 0.115 0.0144 7.99 1.33e- 3
#> 4 A_1 215. 35.3 6.08 3.69e- 3
#> 5 xc_2 405. 0.0126 32038. 5.70e-18
#> 6 wG_2 0.170 0.0368 4.61 9.99e- 3
#> 7 A_2 208. 47.2 4.41 1.16e- 2