iris: Iris Data Set

In rebmix: Finite Mixture Modeling, Clustering & Classification

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.

Source

A. Asuncion and D. J. Newman. Uci machine learning repository, 2007. http://archive.ics.uci.edu/ml/.

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

set.seed(5)

Iris <- split(p = 0.6, Dataset = iris, class = 5)

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

n <- range(a.ntrain(Iris))

irisest <- REBMIX(model = "REBMVNORM",
  Dataset = a.train(Iris),
  Preprocessing = "histogram",
  cmax = 10,
  Criterion = "ICL-BIC",
  EMcontrol = new("EM.Control", strategy = "single"))

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

# Selected chunks.

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

iriscla

summary(iriscla)

# Plot selected chunks.

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

## End(Not run)

rebmix documentation built on July 28, 2021, 5:08 p.m.