NetworkChange implements Bayesian multiple changepoint models to network time series data using a degree-corrected multilinear tensor decomposition method
NetworkChange(
Y,
R = 2,
m = 1,
initial.s = NULL,
mcmc = 100,
burnin = 100,
verbose = 0,
thin = 1,
reduce.mcmc = NULL,
degree.normal = "eigen",
UL.Normal = "Orthonormal",
DIC = FALSE,
Waic = FALSE,
marginal = FALSE,
plotUU = FALSE,
plotZ = FALSE,
constant = FALSE,
b0 = 0,
B0 = 1,
c0 = NULL,
d0 = NULL,
u0 = NULL,
u1 = NULL,
v0 = NULL,
v1 = NULL,
a = NULL,
b = NULL
)An mcmc object that contains the posterior sample. This object can
be summarized by functions provided by the coda package. The object
contains an attribute Waic.out that contains results of WAIC and the log-marginal
likelihood of the model (logmarglike). The object
also contains an attribute prob.state storage matrix that contains the
probability of \(state_i\) for each period
Reponse tensor
Dimension of latent space. The default is 2.
Number of change point.
If m = 0 is specified, the result should be the same as NetworkStatic.
The starting value of latent state vector. The default is sampling from equal probabilities for all states.
The number of MCMC iterations after burnin.
The number of burn-in iterations for the sampler.
A switch which determines whether or not the progress of the
sampler is printed to the screen. If verbose is greater than 0 the
iteration number, the \(\beta\) vector, and the error variance are
printed to the screen every verboseth iteration.
The thinning interval used in the simulation. The number of MCMC iterations must be divisible by this value.
The number of reduced MCMC iterations for marginal likelihood computations.
If reduce.mcmc = NULL, mcmc/thin is used.
A null model for degree correction. Users can choose "NULL", "eigen" or "Lsym." "NULL" is no degree correction. "eigen" is a principal eigen-matrix consisting of the first eigenvalue and the corresponding eigenvector. " Lsym" is a modularity matrix. Default is "eigen."
Transformation of sampled U. Users can choose "NULL", "Normal" or "Orthonormal." "NULL" is no normalization. "Normal" is the standard normalization. "Orthonormal" is the Gram-Schmidt orthgonalization. Default is "NULL."
If DIC = TRUE, the deviation information criterion is computed.
If Waic = TRUE, the Watanabe information criterion is computed.
If marginal = TRUE, the log marignal likelihood is computed using the method of Chib (1995).
If plotUU = TRUE and verbose > 0,
then the plot of the latent space will be
printed to the screen at every verboseth iteration.
The default is plotUU = FALSE.
If plotZ = TRUE and verbose > 0,
then the plot of the degree-corrected input matrix will be
printed to the screen with the sampled mean values at every verboseth iteration.
The default is plotUU = FALSE.
If constant = TRUE, constant parameter is sampled
and saved in the output as attribute bmat. Default is constant = FALSE.
The prior mean of \(\beta\). This must be a scalar. The default value is 0.
The prior variance of \(\beta\). This must be a scalar. The default value is 1.
= 0.1
= 0.1
\(u_0/2\) is the shape parameter for the inverse Gamma prior on variance parameters for U. The default is 10.
\(u_1/2\) is the scale parameter for the inverse Gamma prior on variance parameters for U. The default is 1.
\(v_0/2\) is the shape parameter for the inverse Gamma prior on variance parameters for V. The default is 10.
\(v_1/2\) is the scale parameter for the inverse Gamma prior on variance parameters for V. The default is the time length of Y.
\(a\) is the shape1 beta prior for transition probabilities. By default, the expected duration is computed and corresponding a and b values are assigned. The expected duration is the sample period divided by the number of states.
\(b\) is the shape2 beta prior for transition probabilities. By default, the expected duration is computed and corresponding a and b values are assigned. The expected duration is the sample period divided by the number of states.
Jong Hee Park and Yunkyun Sohn. 2020. "Detecting Structural Change in Longitudinal Network Data." Bayesian Analysis. Vol.15, No.1, pp.133-157.
Peter D. Hoff 2011. "Hierarchical Multilinear Models for Multiway Data." Computational Statistics & Data Analysis. 55: 530-543.
Siddhartha Chib. 1998. "Estimation and comparison of multiple change-point models." Journal of Econometrics. 86: 221-241.
NetworkStatic