clust_cp_epi: Clustering Epidemiological epidemic_diffusions with common changes in time

Description

Clustering Epidemiological epidemic_diffusions with common changes in time

Usage

clust_cp_epi(
  data,
  n_iterations,
  M,
  B,
  L,
  xi = 1/8,
  alpha_SM = 1,
  q = 0.1,
  a0 = 4,
  b0 = 10,
  I0_var = 0.01,
  avg_blk = 0.003,
  print_progress = TRUE,
  user_seed = 1234L
)

Value

Function clust_cp_epi 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 matrix where each slice is a matrix with the orders associated to the output cluster of that iteration.
time computational time in seconds.
$llik a matrix containing the log-likelihood of each population at each iteration.
$rho traceplot for the proportion of infected individuals at time 0.

Arguments

data: a matrix where each entry is the number of infected for a population (row) at a specific discrete time (column).
n_iterations: Second value
M: number of Monte Carlo iterations when computing the likelihood of the epidemic diffusion.
B: number of orders for the normalisation constant.
L: number of split-merge steps for the proposal step.
xi: recovery rate fixed constant for each population at each time.
alpha_SM: $\alpha$ parameter for the main split-merge algorithm.
q: probability of performing a split when updating the single order for the proposal procedure.
a0, b0: parameters for the computation of the integrated likelihood of the epidemic_diffusions.
I0_var: variance for the Metropolis-Hastings estimation of the proportion of infected at time 0.
avg_blk: average number of change points for the random generated orders.
print_progress: If TRUE (default) print the progress bar.
user_seed: seed for random distribution generation.