Description Usage Arguments Details Value References Examples

Visualization of the result of spect_em_lmm().

1 | ```
show_lmm_curve(spect_em_lmm_res, x, y, mix_ratio_init, mu_init, gam_init)
``` |

`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 |

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

Show the fitting curve and variation of the parameters.

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 highâ€“throughput peak separation method in XPS analysis. Science and Technology of Advanced Materials: Methods, 1(1), 45-55.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | ```
#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-6, 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))
``` |

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