iris {rebmix} | R Documentation |
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.
data("iris")
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
discrete iris-setosa
, iris-versicolour
or iris-virginica
.
A. Asuncion and D. J. Newman. Uci machine learning repository, 2007. http://archive.ics.uci.edu/ml.
R. A. Fisher. The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2):179-188, 1936.
## Not run: devAskNewPage(ask = TRUE) data("iris") # Show level attributes. levels(iris[["Class"]]) # Split dataset into train (75 set.seed(5) Iris <- split(p = 0.75, 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) ## End(Not run)