iris {rebmix} | R Documentation |
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:
-
Sepal.Length
continuous. -
Sepal.Width
continuous. -
Petal.Length
continuous. -
Petal.Width
continuous. -
Class
discreteiris-setosa
,iris-versicolour
oriris-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)