The goal of EMpeaks is to efficiently perform the peak fitting on a large number of spectral data. The EMpeaks can support the investigation of peak fitting with two advantages: (1) a large amount of data can be processed at high speed; and (2) stable and automatic calculation can be easily performed.
In version 0.2.0, (1) Lorentz mixture model, (2) Pseudo-Voigt mixture model, and (3) Doniach-Sunjic-Gauss mixture model are available for peak fitting. As these models are generally used in spectroscopy, applicability of the EMpeaksR package is expanded.
You can install the released version of EMpeaksR from CRAN with:
This is a basic example which shows you how to solve a common problem:
library(EMpeaksR)
#generating the synthetic spectral data based on three component Gausian mixture model.
x <- seq(0, 100, by = 0.5)
true_mu <- c(35, 50, 65)
true_sigma <- c(3, 3, 3)
true_mix_ratio <- rep(1/3, 3)
degree <- 4
y <- c(true_mix_ratio[1] * dnorm(x = x, mean = true_mu[1], sd = true_sigma[1])*10^degree +
true_mix_ratio[2] * dnorm(x = x, mean = true_mu[2], sd = true_sigma[2])*10^degree +
true_mix_ratio[3] * dnorm(x = x, mean = true_mu[3], sd = true_sigma[3])*10^degree)
plot(y~x, main = "genrated synthetic spectral data")
#Peak fitting by EMpeaksR
#Initial values
K <- 3
mix_ratio_init <- c(0.2, 0.4, 0.4)
mu_init <- c(20, 40, 70)
sigma_init <- c(2, 5, 4)
#Coducting calculation
SP_EM_G_res <- spect_em_gmm(x, y, mu = mu_init, sigma = sigma_init, mix_ratio = mix_ratio_init,
conv.cri = 1e-8, maxit = 100000)
#Plot fitting curve and trace plot of parameters
show_gmm_curve(SP_EM_G_res, x, y, mix_ratio_init, mu_init, sigma_init)
#Showing the result of spect_em_gmm()
print(cbind(c(mu_init), c(sigma_init), c(mix_ratio_init)))
print(cbind(SP_EM_G_res$mu, SP_EM_G_res$sigma, SP_EM_G_res$mix_ratio))
print(cbind(true_mu, true_sigma, true_mix_ratio))Moreover, this is an example which shows you how to solve a problem using Lorentz mixture model:
#generating the synthetic spectral data based on three component Lorentz mixture model.
x <- seq(0, 100, by = 0.5)
true_mu <- c(35, 50, 65)
true_gam <- c(3, 3, 3)
true_mix_ratio <- rep(1/3, 3)
degree <- 4
#Normalized Lorentz distribution
dCauchy <- function(x, mu, gam) {
(dcauchy(x, mu, gam)) / sum(dcauchy(x, mu, gam))
}
y <- c(true_mix_ratio[1] * dCauchy(x = x, mu = true_mu[1], gam = true_gam[1])*10^degree +
true_mix_ratio[2] * dCauchy(x = x, mu = true_mu[2], gam = true_gam[2])*10^degree +
true_mix_ratio[3] * dCauchy(x = x, mu = true_mu[3], gam = true_gam[3])*10^degree)
plot(y~x, main = "genrated synthetic spectral data")
#Peak fitting by EMpeaksR
#Initial values
K <- 3
mix_ratio_init <- c(0.2, 0.4, 0.4)
mu_init <- c(20, 40, 70)
gam_init <- c(2, 5, 4)
#Coducting calculation
SP_ECM_L_res <- spect_em_lmm(x, y, mu = mu_init, gam = gam_init, mix_ratio = mix_ratio_init,
conv.cri = 1e-8, maxit = 100000)
#Plot fitting curve and trace plot of parameters
show_lmm_curve(SP_ECM_L_res, x, y, mix_ratio_init, mu_init, gam_init)
#Showing the result of spect_em_lmm()
print(cbind(c(mu_init), c(gam_init), c(mix_ratio_init)))
print(cbind(SP_ECM_L_res$mu, SP_ECM_L_res$gam, SP_ECM_L_res$mix_ratio))
print(cbind(true_mu, true_gam, true_mix_ratio))