spect_em_gmm {EMpeaksR} | R Documentation |
Spectrum adapted EM algorithm by GMM
Description
Perform a peak fitting based on the spectrum adapted EM algorithm by Gaussian mixture model.
Usage
spect_em_gmm(x, y, mu, sigma, mix_ratio, conv.cri, maxit)
Arguments
x |
measurement steps |
y |
intensity |
mu |
mean of the components |
sigma |
standard deviation of the components |
mix_ratio |
mixture ratio of the components |
conv.cri |
criterion of the convergence |
maxit |
maximum number of the iteration |
Details
Peak fitting is conducted by spectrum adapted EM algorithm.
Value
mu |
estimated mean of the components |
sigma |
estimated standard deviation of the components |
mix_ratio |
estimated mixture ratio of the components |
it |
number of the iteration to reach the convergence |
LL |
variation of the weighted log likelihood values |
MU |
variation of mu |
SIGMA |
variation of sigma |
MIX_RATIO |
variation of mix_ratio |
W_K |
decomposed component of the spectral data |
convergence |
message for the convergence in the calculation |
cal_time |
calculation time to complete the peak fitting. Unit is seconds |
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.
Examples
#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-2, maxit = 2000)
#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))