These data are the results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars (1-3). The analysis determined the quantities of 13 constituents: alcohol, malic acid, ash, alcalinity of ash, magnesium, total phenols, flavanoids, nonflavanoid phenols, proanthocyanins, colour intensity, hue, OD280/OD315 of diluted wines, and proline found in each of the three types of the wines. The number of instances in classes 1 to 3 is 59, 71 and 48, respectively.
wine is a data frame with 178 cases (rows) and 14 variables (columns) named:
A. Asuncion and D. J. Newman. Uci machine learning repository, 2007. http://archive.ics.uci.edu/ml/.
S. J. Roberts, R. Everson and I. Rezek. Maximum certainty data partitioning. Pattern Recognition, 33(5):833-839, 2000. doi: 10.1016/S0031-3203(99)00086-2.
## Not run: devAskNewPage(ask = TRUE) data(wine) # Show level attributes. levels(factor(wine[["Cultivar"]])) # Split dataset into train (75 set.seed(3) Wine <- split(p = 0.75, Dataset = wine, class = 14) # Estimate number of components, component weights and component # parameters for train subsets. n <- range(a.ntrain(Wine)) K <- c(as.integer(1 + log2(n)), # Minimum v follows Sturges rule. as.integer(10 * log10(n))) # Maximum v follows log10 rule. K <- c(floor(K^(1/13)), ceiling(K^(1/13))) wineest <- REBMIX(model = "REBMVNORM", Dataset = a.train(Wine), Preprocessing = "kernel density estimation", cmax = 10, Criterion = "ICL-BIC", pdf = rep("normal", 13), K = K:K, Restraints = "loose") plot(wineest, pos = 1, nrow = 7, ncol = 6, what = c("pdf")) plot(wineest, pos = 2, nrow = 7, ncol = 6, what = c("pdf")) plot(wineest, pos = 3, nrow = 7, ncol = 6, what = c("pdf")) # Selected chunks. winecla <- RCLSMIX(model = "RCLSMVNORM", x = list(wineest), Dataset = a.test(Wine), Zt = a.Zt(Wine)) winecla summary(winecla) # Plot selected chunks. plot(winecla, nrow = 7, ncol = 6) ## End(Not run)
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