REBMIX-methods | R Documentation |
Returns as default the REBMIX algorithm output for mixtures of conditionally independent normal, lognormal, Weibull, gamma, Gumbel, binomial, Poisson, Dirac, uniform or von Mises component densities. If model
equals "REBMVNORM"
output for mixtures of multivariate normal component densities with unrestricted variance-covariance matrices is returned.
## S4 method for signature 'REBMIX' REBMIX(model = "REBMIX", Dataset = list(), Preprocessing = character(), cmax = 15, cmin = 1, Criterion = "AIC", pdf = character(), theta1 = numeric(), theta2 = numeric(), theta3 = numeric(), K = "auto", ymin = numeric(), ymax = numeric(), ar = 0.1, Restraints = "loose", EMcontrol = NULL, ...) ## ... and for other signatures ## S4 method for signature 'REBMIX' summary(object, ...) ## ... and for other signatures
model |
see Methods section below. |
Dataset |
a list of length n_{\mathrm{D}} of data frames or objects of class |
Preprocessing |
a character giving the preprocessing type. One of |
cmax |
maximum number of components c_{\mathrm{max}} > 0. The default value is |
cmin |
minimum number of components c_{\mathrm{min}} > 0. The default value is |
Criterion |
a character giving the information criterion type. One of default Akaike |
pdf |
a character vector of length d containing continuous or discrete parametric family types. One of |
theta1 |
a vector of length d containing initial component parameters. One of n_{il} = \textrm{number of categories} - 1 for |
theta2 |
a vector of length d containing initial component parameters. Currently not used. |
theta3 |
a vector of length d containing initial component parameters. One of ξ_{il} \in \{-1, \textrm{NA}, 1\} for |
K |
a character or a vector or a matrix of size n_{\mathrm{D}} \times d containing numbers of bins v or v_{1}, …, v_{d} for the histogram and the kernel density estimation or numbers of nearest
neighbours k for the k-nearest neighbour. There is no genuine rule to identify v or k. Consequently,
the REBMIX algorithm identifies them from the set |
ymin |
a vector of length d containing minimum observations. The default value is |
ymax |
a vector of length d containing maximum observations. The default value is |
ar |
acceleration rate 0 < a_{\mathrm{r}} ≤q 1. The default value is |
Restraints |
a character giving the restraints type. One of |
EMcontrol |
an object of class |
object |
see Methods section below. |
... |
currently not used. |
Returns an object of class REBMIX
or REBMVNORM
.
signature(model = "REBMIX")
a character giving the default class name "REBMIX"
for mixtures of conditionally independent normal, lognormal, Weibull, gamma, Gumbel, binomial, Poisson, Dirac, uniform or von Mises component densities.
signature(model = "REBMVNORM")
a character giving the class name "REBMVNORM"
for mixtures of multivariate normal component densities with unrestricted variance-covariance matrices.
signature(object = "REBMIX")
an object of class REBMIX
.
signature(object = "REBMVNORM")
an object of class REBMVNORM
.
Marko Nagode
H. A. Sturges. The choice of a class interval. Journal of American Statistical Association, 21(153):
65-66, 1926. https://www.jstor.org/stable/2965501.
P. F. Velleman. Interactive computing for exploratory data analysis I: display algorithms. Proceedings of the Statistical Computing Section,
American Statistical Association, 1976.
W. J. Dixon and R. A. Kronmal. The Choice of origin and scale for graphs. Journal of the ACM, 12(2):
259-261, 1965. doi: 10.1145/321264.321277.
M. Nagode and M. Fajdiga. A general multi-modal probability density function suitable for the
rainflow ranges of stationary random processes. International Journal of Fatigue, 20(3):211-223,
1998. doi: 10.1016/S0142-1123(97)00106-0.
M. Nagode and M. Fajdiga. An improved algorithm for parameter estimation suitable for mixed
weibull distributions. International Journal of Fatigue, 22(1):75-80, 2000. doi: 10.1016/S0142-1123(99)00112-7.
M. Nagode, J. Klemenc and M. Fajdiga. Parametric modelling and scatter prediction of rainflow
matrices. International Journal of Fatigue, 23(6):525-532, 2001. doi: 10.1016/S0142-1123(01)00007-X.
M. Nagode and M. Fajdiga. An alternative perspective on the mixture estimation problem. Reliability
Engineering & System Safety, 91(4):388-397, 2006. doi: 10.1016/j.ress.2005.02.005.
M. Nagode and M. Fajdiga. The rebmix algorithm for the univariate finite mixture estimation.
Communications in Statistics - Theory and Methods, 40(5):876-892, 2011a. doi: 10.1080/03610920903480890.
M. Nagode and M. Fajdiga. The rebmix algorithm for the multivariate finite mixture estimation.
Communications in Statistics - Theory and Methods, 40(11):2022-2034, 2011b. doi: 10.1080/03610921003725788.
M. Nagode. Finite mixture modeling via REBMIX.
Journal of Algorithms and Optimization, 3(2):14-28, 2015. https://repozitorij.uni-lj.si/Dokument.php?id=127674&lang=eng.
B. Panic, J. Klemenc, M. Nagode. Improved initialization of the EM algorithm for mixture model parameter estimation.
Mathematics, 8(3):373, 2020.
doi: 10.3390/math8030373.
# Generate and plot univariate normal dataset. n <- c(998, 263, 1086, 487) Theta <- new("RNGMIX.Theta", c = 4, pdf = "normal") a.theta1(Theta) <- c(688, 265, 30, 934) a.theta2(Theta) <- c(72, 54, 34, 28) normal <- RNGMIX(Dataset.name = "complex1", rseed = -1, n = n, Theta = a.Theta(Theta)) normal a.Dataset(normal, 1)[1:20,] # Estimate number of components, component weights and component parameters. normalest <- REBMIX(Dataset = a.Dataset(normal), Preprocessing = "h", cmax = 8, Criterion = "BIC", pdf = "n") normalest BIC(normalest) logL(normalest) # Plot finite mixture. plot(normalest, nrow = 2, what = c("pdf", "marginal cdf"), npts = 1000) # EM algorithm utilization # Load iris data. data(iris) Dataset <- list(data.frame(iris[, c(1:4)])) # Create EM.Control object. EM <- new("EM.Control", strategy = "exhaustive", variant = "EM", acceleration = "fixed", tolerance = 1e-4, acceleration.multiplier = 1.0, maximum.iterations = 1000) # Mixture parameter estimation using REBMIX and EM algorithm. irisest <- REBMIX(model = "REBMVNORM", Dataset = Dataset, Preprocessing = "histogram", cmax = 10, Criterion = "BIC", EMcontrol = EM) irisest # Print total number of EM iterations used in Ehxaustive strategy from summary.EM slot. a.summary.EM(irisest, col.name = "total.iterations.nbr", pos = 1)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.