rebmix (version 2.10.2)

RCLRMIX-class: Class "RCLRMIX"

Description

Object of class RCLRMIX.

Objects from the Class

Objects can be created by calls of the form new("RCLRMIX", ...).

Slots

x:

an object of class REBMIX.

pos:

a desired row number in x@summary to be clustered. The default value is 1.

Zt:

a factor of true cluster membership.

Zp:

a factor of predictive cluster membership.

c:

number of clusters.

prob:

a vector of length \(c\) containing probabilities of correct clustering for \(s = 1, \ldots, c\).

from:

a vector of length \(c - 1\) containing clusters merged to to clusters.

to:

a vector of length \(c - 1\) containing clusters originating from from clusters.

EN:

a vector of length \(c - 1\) containing entropies for combined clusters.

ED:

a vector of length \(c - 1\) containing decrease of entropies for combined clusters.

References

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. http://dx.doi.org/10.1198/jcgs.2010.08111

Examples

# NOT RUN {
devAskNewPage(ask = TRUE)

# Generate normal dataset.

n <- c(500, 200, 400)

Theta <- list(pdf1 = rep("normal", 2),
  theta1.1 = c(3, 10),
  theta2.1 = c(3, 0.3, 0.3, 2),
  pdf2 = rep("normal", 2),
  theta1.2 = c(8, 6),
  theta2.2 = c(5.7, -2.3, -2.3, 3.5),
  pdf3 = rep("normal", 2),
  theta1.3 = c(12, 11),
  theta2.3 = c(2, 1, 1, 2))

normal <- RNGMIX(model = "RNGMVNORM", Dataset.name = "normal_1", n = n, Theta = Theta)

# Number of classes or nearest neighbours to be processed.

K <- c(as.integer(1 + log2(sum(n))), # Minimum v follows Sturges rule.
  as.integer(10 * log10(sum(n)))) # Maximum v follows log10 rule.

# Estimate number of components, component weights and component parameters.

normalest <- REBMIX(model = "REBMVNORM",
  Dataset = normal@Dataset,
  Preprocessing = "histogram",
  cmax = 6,
  Criterion = "BIC",
  pdf = rep("normal", 2),
  K = K[1]:K[2])

summary(normalest)

# Plot finite mixture.

plot(normalest)

# Cluster dataset.

normalclu <- RCLRMIX(model = "RCLRMVNORM", x = normalest, Zt = normal@Zt)

# Plot clusters.

plot(normalclu)

summary(normalclu)
# }