BayesChange (version 2.1.3)

plot.ClustCpObj: Plot estimated partition

Description

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

Usage

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

Value

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

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.

Examples

# \donttest{
## 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)


## Epidemic diffusions

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)
# }