# Prediction of future state visits Calculates the transition probability matrix associated with the M-step transition. ```r click.predict(M = 1, gamma, pr = NULL) ``` ## Arguments - `M`: number of transition steps (M = 1 by default) - `gamma`: array of transition probabilities (p x p x K) - `pr`: vector of probabilities associated with components (length K) ## Details Returns a transition probability matrix associated with the M-step transition. If the vector pr is not specified, all components are assumed equally likely. ## References 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. ## Author(s) Melnykov, V. ## See Also click.EM ## Examples ```r set.seed(123) n.seq <- 200 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$S) # EM ALGORITHM M2 <- click.EM(X = C$X, y = C$y, K = 2) # Assuming component probabilities given by mixing proportions, predict the next state click.predict(M = 1, gamma = M2$gamma, pr = M2$alpha) # For the last location in the first sequence, predict the three-step transition # location, given corresponding posterior probabilities click.predict(M = 3, gamma = M2$gamma, pr = M2$z[1,])[A$S[[1]][length(A$S[[1]])],] ```

Predict function