fit_gaussian_nlm: Fit a Gaussian Peak Using Non-Linear Least Squares
In pmbrophy/SummitR: Provides a set of tools for peak fitting

Description Usage Arguments Details Value Examples

View source: R/FitFunctions_gaussian_nlm.R

Fit a Gaussian Peak Using Non-Linear Least Squares

1	fit_gaussian_nlm(x, y, init_mu, init_sigma, init_k)

`x`	values of the x-axis
`y`	values of the y-axis
`init_mu`	initial guess of mu (the peak center)
`init_sigma`	initial guess of sigma (the standard deviation)
`init_k`	initial guess of k (the peak height)

Implementation uses the nls() function to fit a gaussian peak to experimental data using the nl2sol algorithm from the Port library - see ?nls() and https://people.sc.fsu.edu/~jburkardt/f_src/minpack/minpack.html

a list containing a vector 'fitPeak' containing the y-values of the fitted peak and a list 'nlsResult' containing the data returned from nlm()

 xVec <- seq(from = 1, to = 100, by = 0.1)
 gauss <- func_gaussian(x = xVec, mu = 10, sigma = 1, probDensity = FALSE, k = 10)
 gauss <- gauss + rnorm(n = length(gauss), mean = 0, sd = 1)
 fit <- fit_gaussian_nlm(x = xVec, y = gauss, init_mu = 9, init_sigma = 0.5, init_k = 3)
 plot(x = xVec, y = gauss); points(x = xVec, y = fit$fitPeak, col = "red")