deconvolve_fff: Deconvolve incompletely resolved peaks

In zhengfj1994/MeTEA: Targeted extraction from untargeted metabolomics dataset by using metabolomics database

Description

Deconvolve incompletely resolved peaks

Usage

deconvolve_fff(
  x,
  y,
  h,
  mu,
  s,
  g = NULL,
  fn = "skew_gaussian",
  algorithm = "port",
  iter = 300,
  tol = 0.1
)

Arguments

x	A vector of retention times.
y	A vector of detector responses.
h	A vector of guesses for peak height, with length equal to the desired number of peaks.
mu	A vector of guesses for peak means, with length equal to the desired number of peaks.
s	A vector of guesses for peak standard deviations, with length equal to the desired number of peaks.
g	A vector of guesses for peak shape parameters, with length equal to the desired number of peaks. Silently ignored if 'fn == "normal'.
fn	Function for peak fitting: Gaussian, skew Gaussian or exponentially modified Gaussian ("normal", "skew_gaussian" or "emg").
algorithm	Refer to nls() in the 'stats' package.
iter	Refer to nls() in the 'stats' package.
tol	Refer to nls() in the 'stats' package.

Value

A list containing the nls() model, the fitted model evaluated at x, and a tibble with a column for each of the component peaks evaluated at x.

Examples

set.seed(0)
x <- seq(0, 10, length.out = 100)
# two gaussian peaks:
y <- exp(-(x - 3) ^ 2 / 2) + exp(-(x - 7) ^ 2 / 2) + rnorm(100, 0, .1)
deconvolve_fff(x, y, h = c(1, 1), mu = c(3, 7), s = c(1, 1), fn = "normal")

zhengfj1994/MeTEA documentation built on June 29, 2021, 5:21 a.m.