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.

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.

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]