plot.ClustCpObj: Plot estimated partition

In BayesChange: Bayesian Methods for Change Points Analysis

Plot estimated partition

Description

The plot method plots the estimates partition through the salso algorithm, for a ClustCpObj class object.

Usage

## S3 method for class 'ClustCpObj'
plot(
  x,
  y = NULL,
  loss = "VI",
  maxNClusters = 0,
  nRuns = 16,
  maxZealousAttempts = 10,
  ...
)

Arguments

x	an object of class ClustCpObj.
y	parameter of the generic method.
loss	The loss function used to estimate the final partition, it can be "VI", "binder", "omARI", "NVI", "ID", "NID".
maxNClusters	maximum number of clusters in salso procedure.
nRuns	number of runs in salso procedure.
maxZealousAttempts	maximum number of zealous attempts in salso procedure.
...	parameter of the generic method.

Value

The function returns a ggplot object representing the time series or the survival functions colored according to the final partition.

Examples

## Time series

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(data = data_mat, n_iterations = 5000, n_burnin = 1000,
                params = list(L = 1, B = 1000, phi = 0.5), kernel = "ts")

plot(out)


## Survival functions

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(data = data_mat, n_iterations = 100, n_burnin = 10,
                params = list(M = 100, L = 1, B = 100), kernel = "epi")

plot(out)

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