mapclusters: Map Clusters

In rebmix: Finite Mixture Modeling, Clustering & Classification

Map Clusters

Description

Returns a factor of predictive cluster membership for dataset.

Usage

## S4 method for signature 'RCLRMIX'
mapclusters(x = NULL, Dataset = data.frame(),
            s = expression(c), ...)
## ... and for other signatures

Arguments

x	see Methods section below.
Dataset	a data frame of size n \times d containing d-dimensional dataset. Each of the d columns represents one random variable. Number of observations n equal the number of rows in the dataset.
s	a desired number of clusters to be created. The default value is expression(c).
...	currently not used.

Methods

signature(x = "RCLRMIX")

an object of class RCLRMIX.

signature(x = "RCLRMVNORM")

an object of class RCLRMVNORM.

Author(s)

Marko Nagode, Branislav Panic

Examples

devAskNewPage(ask = TRUE)

# Generate normal dataset.

n <- c(50, 20, 40)

Theta <- new("RNGMVNORM.Theta", c = 3, d = 2)

a.theta1(Theta, 1) <- c(3, 10)
a.theta1(Theta, 2) <- c(8, 6)
a.theta1(Theta, 3) <- c(12, 11)
a.theta2(Theta, 1) <- c(3, 0.3, 0.3, 2)
a.theta2(Theta, 2) <- c(5.7, -2.3, -2.3, 3.5)
a.theta2(Theta, 3) <- c(2, 1, 1, 2)

normal <- RNGMIX(model = "RNGMVNORM", Dataset.name = paste("normal_", 1:10, sep = ""),
  n = n, Theta = a.Theta(Theta))

# Convert all datasets to single histogram.

hist <- NULL

n <- length(normal@Dataset)

hist <- fhistogram(Dataset = normal@Dataset[[1]], K = c(10, 10), 
  ymin = a.ymin(normal), ymax = a.ymax(normal))

for (i in 2:n) {
  hist <- fhistogram(x = hist, Dataset = normal@Dataset[[i]], shrink = i == n)
}

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

normalest <- REBMIX(model = "REBMVNORM",
  Dataset = list(hist),
  Preprocessing = "histogram",
  cmax = 6,
  Criterion = "BIC")

summary(normalest)

# Plot finite mixture.

plot(normalest)

# Cluster dataset.

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

# Plot clusters.

plot(normalclu)

summary(normalclu)

# Map clusters.

Zp <- mapclusters(x = normalclu, Dataset = a.Dataset(normal, 4))

Zt <- a.Zt(normal)

Zp

Zt

rebmix documentation built on Aug. 18, 2022, 1:06 a.m.