BayesChange (version 2.1.3)

clust_cp_multi: Clustering multivariate times series with common changes in time

Description

Clustering multivariate times series with common changes in time

Usage

clust_cp_multi(
  data,
  n_iterations,
  B,
  L,
  phi,
  k_0,
  nu_0,
  S_0,
  m_0,
  q = 0.5,
  alpha_SM = 0.1,
  print_progress = TRUE,
  user_seed = 1234L
)

Value

Function clust_cp_multi returns a list containing the following components:

• $clust a matrix where each row corresponds to the output cluster of the corresponding iteration.

• $orders a multidimensional array where each slice is a matrix and represent an iteration. The row of each matrix correspond the order associated to the corresponding cluster.

• time computational time in seconds.

• $norm_vec a vector containing the normalisation constant computed at the beginning of the algorithm.

Arguments

data

a multidimensional matrix where each element is a matrix whose rows are the observations and columns the dimensions.

n_iterations

number of MCMC iterations.

B

number of orders for the normalisation constant.

L

number of split-merge steps for the proposal step.

phi, k_0, nu_0, S_0, m_0

parameters of the integrated likelihood.

q

probability of a split in the split-merge proposal and acceleration step.

alpha_SM

\(\alpha\) for the main split-merge algorithm.

print_progress

If TRUE (default) print the progress bar.

user_seed

seed for random distribution generation.

Examples

# \donttest{
data_array <- array(data = NA, dim = c(3,100,5))

data_array[1,,1] <- as.numeric(c(rnorm(50,0,0.100), rnorm(50,1,0.250)))
data_array[2,,1] <- as.numeric(c(rnorm(50,0,0.100), rnorm(50,1,0.250)))
data_array[3,,1] <- as.numeric(c(rnorm(50,0,0.100), rnorm(50,1,0.250)))

data_array[1,,2] <- as.numeric(c(rnorm(50,0,0.100), rnorm(50,1,0.250)))
data_array[2,,2] <- as.numeric(c(rnorm(50,0,0.100), rnorm(50,1,0.250)))
data_array[3,,2] <- as.numeric(c(rnorm(50,0,0.100), rnorm(50,1,0.250)))

data_array[1,,3] <- as.numeric(c(rnorm(50,0,0.175), rnorm(50,1,0.280)))
data_array[2,,3] <- as.numeric(c(rnorm(50,0,0.175), rnorm(50,1,0.280)))
data_array[3,,3] <- as.numeric(c(rnorm(50,0,0.175), rnorm(50,1,0.280)))

data_array[1,,4] <- as.numeric(c(rnorm(25,0,0.135), rnorm(75,1,0.225)))
data_array[2,,4] <- as.numeric(c(rnorm(25,0,0.135), rnorm(75,1,0.225)))
data_array[3,,4] <- as.numeric(c(rnorm(25,0,0.135), rnorm(75,1,0.225)))

data_array[1,,5] <- as.numeric(c(rnorm(25,0,0.155), rnorm(75,1,0.280)))
data_array[2,,5] <- as.numeric(c(rnorm(25,0,0.155), rnorm(75,1,0.280)))
data_array[3,,5] <- as.numeric(c(rnorm(25,0,0.155), rnorm(75,1,0.280)))

out <- clust_cp_multi(data = data_array, n_iterations = 3000, B = 1000, L = 1,
                        phi = 0.1, k_0 = 0.25, nu_0 = 5, S_0 = diag(0.1,3,3), m_0 = rep(0,3))
# }