View source: R/MixtureFitting.R
smm_fit_em_GNL08 | R Documentation |
Estimates parameters for univariate Student's t mixture using Expectation Maximization algorithm, according to Eqns. 12–17 of Gerogiannis et al. (2009).
smm_fit_em_GNL08( x, p, epsilon = c( 1e-6, 1e-6, 1e-6, 1e-6 ),
collect.history = FALSE, debug = FALSE,
min.sigma = 1e-256, min.ni = 1e-256,
max.df = 1000, max.steps = Inf,
polyroot.solution = 'jenkins_taub',
convergence = abs_convergence,
unif.component = FALSE )
x |
data vector |
p |
initialization vector of 4*n parameters, where n is number of mixture components. Structure of p vector is p = c( A1, A2, ..., An, mu1, mu2, ..., mun, k1, k2, ..., kn, ni1, ni2, ..., nin ), where Ai is the proportion of i-th component, mui is the center of i-th component, ki is the concentration of i-th component and nii is the degrees of freedom of i-th component. |
epsilon |
tolerance threshold for convergence. Structure of epsilon is epsilon = c( epsilon_A, epsilon_mu, epsilon_k, epsilon_ni ), where epsilon_A is threshold for component proportions, epsilon_mu is threshold for component centers, epsilon_k is threshold for component concentrations and epsilon_ni is threshold for component degrees of freedom. |
collect.history |
logical. If set to TRUE, a list of parameter values of all iterations is returned. |
debug |
flag to turn the debug prints on/off. |
min.sigma |
minimum value of sigma |
min.ni |
minimum value of degrees of freedom |
max.df |
maximum value of degrees of freedom |
max.steps |
maximum number of steps, may be infinity |
polyroot.solution |
polyroot finding method used to approximate digamma function. Possible values are 'jenkins_taub' and 'newton_raphson'. |
convergence |
function to use for convergence checking. Must accept function values of the last two iterations and return TRUE or FALSE. |
unif.component |
should a uniform component for outliers be added, as suggested by Cousineau & Chartier (2010)? |
A list.
Andrius Merkys
Gerogiannis, D.; Nikou, C. & Likas, A. The mixtures of Student's t-distributions as a robust framework for rigid registration. Image and Vision Computing, Elsevier BV, 2009, 27, 1285–1294 https://www.cs.uoi.gr/~arly/papers/imavis09.pdf
Cousineau, D. & Chartier, S. Outliers detection and treatment: a review. International Journal of Psychological Research, 2010, 3, 58–67 https://revistas.usb.edu.co/index.php/IJPR/article/view/844
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