The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
BayesChange provides C++ functions to perform Bayesian
change points analysis.
To install BayesChange the package devtools
is needed.
install.packages("devtools")Now BayesChange can be installed through the GitHub repository
of the package:
devtools::install_github("lucadanese/BayesChange")The package contains two main functions:
detect_cp detect change points on univariate and
multivariate time series.clust_cp cluster time series or survival functions with
common change points.Additional methods and functions are included:
print() and summary() give informations
about the algorithm.posterior_estimate() estimates the change points or the
final partition of the data.plot() provides a graphical representation of the
results.sim_epi_data generates an arbitrary number of simulated
survival functions.library(BayesChange)
## Univariate time series
data_vec <- as.numeric(c(rnorm(50,0,0.1), rnorm(50,1,0.25)))
out <- detect_cp(data = data_vec, n_iterations = 2500, n_burnin = 500,
params = list(q = 0.25, phi = 0.1, a = 1, b = 1, c = 0.1))
print(out)
summary(out)
posterior_estimate(out)
plot(out)
## Multivariate time series
data_mat <- matrix(NA, nrow = 3, 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)))
out <- detect_cp(data = data_mat, n_iterations = 2500, n_burnin = 500,
params = list(q = 0.25, k_0 = 0.25, nu_0 = 4, phi_0 = diag(1,3,3), m_0 = rep(0,3),
par_theta_c = 2, par_theta_d = 0.2, prior_var_gamma = 0.1))
print(out)
summary(out)
posterior_estimate(out)
plot(out)
## Univariate 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, gamma = 0.5), kernel = "ts")
print(out)
summary(out)
posterior_estimate(out)
plot(out)
## Multivariate time series
data_array <- array(data = NA, dim = c(3,100,5))
data_array[1,,1] <- as.numeric(c(rnorm(50,0,0.100), rnorm(50,1,0.250)))
data_array[2,,1] <- as.numeric(c(rnorm(50,0,0.100), rnorm(50,1,0.250)))
data_array[3,,1] <- as.numeric(c(rnorm(50,0,0.100), rnorm(50,1,0.250)))
data_array[1,,2] <- as.numeric(c(rnorm(50,0,0.100), rnorm(50,1,0.250)))
data_array[2,,2] <- as.numeric(c(rnorm(50,0,0.100), rnorm(50,1,0.250)))
data_array[3,,2] <- as.numeric(c(rnorm(50,0,0.100), rnorm(50,1,0.250)))
data_array[1,,3] <- as.numeric(c(rnorm(50,0,0.175), rnorm(50,1,0.280)))
data_array[2,,3] <- as.numeric(c(rnorm(50,0,0.175), rnorm(50,1,0.280)))
data_array[3,,3] <- as.numeric(c(rnorm(50,0,0.175), rnorm(50,1,0.280)))
data_array[1,,4] <- as.numeric(c(rnorm(25,0,0.135), rnorm(75,1,0.225)))
data_array[2,,4] <- as.numeric(c(rnorm(25,0,0.135), rnorm(75,1,0.225)))
data_array[3,,4] <- as.numeric(c(rnorm(25,0,0.135), rnorm(75,1,0.225)))
data_array[1,,5] <- as.numeric(c(rnorm(25,0,0.155), rnorm(75,1,0.280)))
data_array[2,,5] <- as.numeric(c(rnorm(25,0,0.155), rnorm(75,1,0.280)))
data_array[3,,5] <- as.numeric(c(rnorm(25,0,0.155), rnorm(75,1,0.280)))
out <- clust_cp(data = data_array, n_iterations = 3000, n_burnin = 1000,
params = list(B = 1000, L = 1, gamma = 0.5, k_0 = 0.25,
nu_0 = 5, phi_0 = diag(0.1,3,3),
m_0 = rep(0,3)), kernel = "ts")
print(out)
summary(out)
posterior_estimate(out)
plot(out)
## Epidemiological data
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 = 3000, n_burnin = 1000,
params = list(M = 250, L = 1, B = 1000), kernel = "epi")
print(out)print(out)
summary(out)
posterior_estimate(out)
plot(out)These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.