mapclusters-methods | R Documentation |
Returns a factor of predictive cluster membership for dataset.
## S4 method for signature 'RCLRMIX' mapclusters(x = NULL, Dataset = data.frame(), s = expression(c), ...) ## ... and for other signatures
x |
see Methods section below. |
Dataset |
a data frame of size n \times d containing d-dimensional dataset. Each of the d columns represents one random variable. Number of observations n equal the number of rows in the dataset. |
s |
a desired number of clusters to be created. The default value is |
... |
currently not used. |
signature(x = "RCLRMIX")
an object of class RCLRMIX
.
signature(x = "RCLRMVNORM")
an object of class RCLRMVNORM
.
Marko Nagode, Branislav Panic
devAskNewPage(ask = TRUE) # Generate normal dataset. n <- c(50, 20, 40) Theta <- new("RNGMVNORM.Theta", c = 3, d = 2) a.theta1(Theta, 1) <- c(3, 10) a.theta1(Theta, 2) <- c(8, 6) a.theta1(Theta, 3) <- c(12, 11) a.theta2(Theta, 1) <- c(3, 0.3, 0.3, 2) a.theta2(Theta, 2) <- c(5.7, -2.3, -2.3, 3.5) a.theta2(Theta, 3) <- c(2, 1, 1, 2) normal <- RNGMIX(model = "RNGMVNORM", Dataset.name = paste("normal_", 1:10, sep = ""), n = n, Theta = a.Theta(Theta)) # Convert all datasets to single histogram. hist <- NULL n <- length(normal@Dataset) hist <- fhistogram(Dataset = normal@Dataset[[1]], K = c(10, 10), ymin = a.ymin(normal), ymax = a.ymax(normal)) for (i in 2:n) { hist <- fhistogram(x = hist, Dataset = normal@Dataset[[i]], shrink = i == n) } # Estimate number of components, component weights and component parameters. normalest <- REBMIX(model = "REBMVNORM", Dataset = list(hist), Preprocessing = "histogram", cmax = 6, Criterion = "BIC") summary(normalest) # Plot finite mixture. plot(normalest) # Cluster dataset. normalclu <- RCLRMIX(model = "RCLRMVNORM", x = normalest) # Plot clusters. plot(normalclu) summary(normalclu) # Map clusters. Zp <- mapclusters(x = normalclu, Dataset = a.Dataset(normal, 4)) Zt <- a.Zt(normal) Zp Zt
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