clust_cp_epi: Clustering Epidemiological survival functions with common...

In BayesChange: Bayesian Methods for Change Points Analysis

Clustering Epidemiological survival functions with common changes in time

Description

Clustering Epidemiological survival functions 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
)

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 survival function.
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 survival functions.
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.

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.

Examples

data_mat <- matrix(NA, nrow = 5, ncol = 50)

betas <- list(c(rep(0.45, 25),rep(0.14,25)),
              c(rep(0.55, 25),rep(0.11,25)),
              c(rep(0.50, 25),rep(0.12,25)),
              c(rep(0.52, 10),rep(0.15,40)),
              c(rep(0.53, 10),rep(0.13,40)))

 inf_times <- list()

 for(i in 1:5){

   inf_times[[i]] <- sim_epi_data(10000, 10, 50, betas[[i]], 1/8)

   vec <- rep(0,50)
   names(vec) <- as.character(1:50)

   for(j in 1:50){
     if(as.character(j) %in% names(table(floor(inf_times[[i]])))){
       vec[j] = table(floor(inf_times[[i]]))[which(names(table(floor(inf_times[[i]]))) == j)]
     }
   }
   data_mat[i,] <- vec
 }

 out <- clust_cp_epi(data = data_mat, n_iterations = 3000, M = 250, B = 1000, L = 1)

BayesChange documentation built on June 10, 2025, 9:09 a.m.