BayesChange (version 2.1.3)

clust_cp_uni: Clustering univariate times series with common changes in time

Description

Clustering univariate times series with common changes in time

Usage

clust_cp_uni(
  data,
  n_iterations,
  B,
  L,
  phi,
  a = 1,
  b = 1,
  c = 1,
  q = 0.5,
  alpha_SM = 0.1,
  print_progress = TRUE,
  user_seed = 1234L
)

Value

Function clust_cp_uni 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 matrix where each row is an observation and each column corresponds to a discrete time.

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, a, b, c

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_mat <- matrix(NA, nrow = 5, ncol = 100)

data_mat[1,] <- as.numeric(c(rnorm(50,0,0.100), rnorm(50,1,0.250)))
data_mat[2,] <- as.numeric(c(rnorm(50,0,0.125), rnorm(50,1,0.225)))
data_mat[3,] <- as.numeric(c(rnorm(50,0,0.175), rnorm(50,1,0.280)))
data_mat[4,] <- as.numeric(c(rnorm(25,0,0.135), rnorm(75,1,0.225)))
data_mat[5,] <- as.numeric(c(rnorm(25,0,0.155), rnorm(75,1,0.280)))

out <- clust_cp_uni(data = data_mat, n_iterations = 5000, B = 1000, L = 1, phi = 0.5)
# }