rebmix (version 2.10.2)

iris: Iris Data Set

Description

This is perhaps the best known database to be found in the pattern recognition literature. Fisher's paper is a classic in the field and is referenced frequently to this day. The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are NOT linearly separable from each other.

Usage

data("iris")

Format

iris is a data frame with 150 cases (rows) and 5 variables (columns) named:

1. Sepal.Length continuous.

2. Sepal.Width continuous.

3. Petal.Length continuous.

4. Petal.Width continuous.

5. Class discrete iris-setosa, iris-versicolour or iris-virginica.

References

R. A. Fisher. The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2):179-188, 1936.

Examples

# NOT RUN {
devAskNewPage(ask = TRUE)

data("iris")

# Show level attributes.

levels(iris[["Class"]])

# Split dataset into train (75<!-- %) and test (25%) subsets. -->

set.seed(5)

Iris <- split(p = 0.75, Dataset = iris, class = 5)

# Estimate number of components, component weights and component 
# parameters for train subsets.

n <- range(Iris@ntrain)

K <- c(as.integer(1 + log2(n[1])), # Minimum v follows Sturges rule.
  as.integer(10 * log10(n[2]))) # Maximum v follows log10 rule.

K <- c(floor(K[1]^(1/4)), ceiling(K[2]^(1/4)))

irisest <- REBMIX(model = "REBMVNORM",
  Dataset = Iris@train,
  Preprocessing = "Parzen window",
  cmax = 10,
  Criterion = "ICL-BIC",
  pdf = rep("normal", 4),
  K = K[1]:K[2])

plot(irisest, pos = 1, nrow = 3, ncol = 2, what = c("den"))
plot(irisest, pos = 2, nrow = 3, ncol = 2, what = c("den"))
plot(irisest, pos = 3, nrow = 3, ncol = 2, what = c("den"))

# Selected chunks.

iriscla <- RCLSMIX(model = "RCLSMVNORM", 
  x = list(irisest),
  Dataset = Iris@test,
  Zt = Iris@Zt)

iriscla

summary(iriscla)

# Plot selected chunks.

plot(iriscla, nrow = 3, ncol = 2)
# }