ClickClust-package {ClickClust} | R Documentation |
The package runs finite mixture modeling and model-based clustering for categorical sequences
Package: | ClickClust |
Type: | Package |
Version: | 1.0 |
Date: | 2014-04-04 |
License: | GPL (>= 2) |
LazyLoad: | no |
Function 'click.EM' runs the EM algorithm for finite mixture models with Markov model components.
Volodymyr Melnykov
Maintainer: Volodymyr Melnykov <vmelnykov@cba.ua.edu>
Melnykov, V. (2016) Model-Based Biclustering of Clickstream Data, Computational Statistics and Data Analysis, 93, 31-45.
Melnykov, V. (2016) ClickClust: An R Package for Model-Based Clustering of Categorical Sequences, Journal of Statistical Software, 74, 1-34.
set.seed(123) n.seq <- 50 p <- 5 K <- 2 mix.prop <- c(0.3, 0.7) TP1 <- matrix(c(0.20, 0.10, 0.15, 0.15, 0.40, 0.20, 0.20, 0.20, 0.20, 0.20, 0.15, 0.10, 0.20, 0.20, 0.35, 0.15, 0.10, 0.20, 0.20, 0.35, 0.30, 0.30, 0.10, 0.10, 0.20), byrow = TRUE, ncol = p) TP2 <- matrix(c(0.15, 0.15, 0.20, 0.20, 0.30, 0.20, 0.10, 0.30, 0.30, 0.10, 0.25, 0.20, 0.15, 0.15, 0.25, 0.25, 0.20, 0.15, 0.15, 0.25, 0.10, 0.30, 0.20, 0.20, 0.20), byrow = TRUE, ncol = p) TP <- array(rep(NA, p * p * K), c(p, p, K)) TP[,,1] <- TP1 TP[,,2] <- TP2 # DATA SIMULATION A <- click.sim(n = n.seq, int = c(10, 50), alpha = mix.prop, gamma = TP) C <- click.read(A$S) # EM ALGORITHM click.EM(X = C$X, K = 2)