LMM_fitting_graph: Visualization of the result of spect_em_lmm

In EMpeaksR: Conducting the Peak Fitting Based on the EM Algorithm

Visualization of the result of spect_em_lmm

Description

Visualization of the result of spect_em_lmm().

Usage

show_lmm_curve(spect_em_lmm_res, x, y, mix_ratio_init, mu_init, gam_init)

Arguments

spect_em_lmm_res	data set obtained by spect_em_lmm()
x	measurement steps
y	intensity
mix_ratio_init	initial values of the mixture ratio of the components
mu_init	initial values of the mean of the components
gam_init	initial values of the scale parameter of the components

Details

Perform a visualization of fitting curve estimated by Lorentz mixture model.

Value

Show the fitting curve and variation of the parameters.

References

Matsumura, T., Nagamura, N., Akaho, S., Nagata, K., & Ando, Y. (2019). Spectrum adapted expectation-maximization algorithm for high-throughput peak shift analysis. Science and technology of advanced materials, 20(1), 733-745.

Matsumura, T., Nagamura, N., Akaho, S., Nagata, K., & Ando, Y. (2021). Spectrum adapted expectation-conditional maximization algorithm for extending high–throughput peak separation method in XPS analysis. Science and Technology of Advanced Materials: Methods, 1(1), 45-55.

Examples

#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-2, maxit = 2000)

#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))

EMpeaksR documentation built on March 31, 2023, 9:37 p.m.