RCLRMIX-methods {rebmix} | R Documentation |
Returns as default the RCLRMIX algorithm output for mixtures of conditionally independent normal, lognormal, Weibull, gamma, binomial, Poisson, Dirac or von Mises component densities, following the methodology proposed in the article cited in the references. If model
equals "RCLRMVNORM"
output for mixtures of multivariate normal component densities with unrestricted variance-covariance matrices is returned.
## S4 method for signature 'RCLRMIX' RCLRMIX(model = "RCLRMIX", x = NULL, pos = 1, Zt = factor(), ...) ## ... and for other signatures ## S4 method for signature 'RCLRMIX' summary(object, ...) ## ... and for other signatures
model |
see Methods section below. |
x |
an object of class |
pos |
a desired row number in |
Zt |
a factor of true cluster membership. The default value is |
object |
see Methods section below. |
... |
currently not used; additional arguments affecting the summary produced. |
Returns an object of class RCLRMIX
or RCLRMVNORM
.
signature(model = "RCLRMIX")
a character giving the default class name "RCLRMIX"
for mixtures of conditionally independent normal, lognormal, Weibull, gamma, binomial, Poisson, Dirac or von Mises component densities.
signature(model = "RCLRMVNORM")
a character giving the class name "RCLRMVNORM"
for mixtures of multivariate normal component densities with unrestricted variance-covariance matrices.
signature(object = "RCLRMIX")
an object of class RCLRMIX
.
signature(object = "RCLRMVNORM")
an object of class RCLRMVNORM
.
Marko Nagode
J. P. Baudry, A. E. Raftery, G. Celeux, K. Lo and R. Gottardo. Combining mixture components for clustering. Journal of Computational and Graphical Statistics, 19(2):332-353, 2010. https://doi.org/10.1198/jcgs.2010.08111
devAskNewPage(ask = TRUE) # Generate Poisson dataset. n <- c(500, 200, 400) Theta <- new("RNGMIX.Theta", c = 3, pdf = "Poisson") a.theta1(Theta) <- c(3, 12, 36) poisson <- RNGMIX(Dataset.name = "Poisson_1", n = n, Theta = a.Theta(Theta)) # Estimate number of components, component weights and component parameters. poissonest <- REBMIX(Dataset = a.Dataset(poisson), Preprocessing = "histogram", cmax = 6, Criterion = "BIC", pdf = rep("Poisson", 1), K = 1) summary(poissonest) # Plot finite mixture. plot(poissonest) # Cluster dataset. poissonclu <- RCLRMIX(x = poissonest, Zt = a.Zt(poisson)) summary(poissonclu) # Plot clusters. plot(poissonclu) summary(poissonclu)