click.predict {ClickClust} | R Documentation |
Prediction of future state visits
Description
Calculates the transition probability matrix associated with the M-step transition.
Usage
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.
Author(s)
Melnykov, V.
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.
See Also
click.EM
Examples
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]])],]
[Package ClickClust version 1.1.6 Index]