NetworkChange {NetworkChange} | R Documentation |
Changepoint analysis of a degree-corrected multilinear tensor model
Description
NetworkChange implements Bayesian multiple changepoint models to network time series data using a degree-corrected multilinear tensor decomposition method
Usage
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
)
Arguments
Y |
Reponse tensor |
R |
Dimension of latent space. The default is 2. |
m |
Number of change point.
If |
initial.s |
The starting value of latent state vector. The default is sampling from equal probabilities for all states. |
mcmc |
The number of MCMC iterations after burnin. |
burnin |
The number of burn-in iterations for the sampler. |
verbose |
A switch which determines whether or not the progress of the
sampler is printed to the screen. If |
thin |
The thinning interval used in the simulation. The number of MCMC iterations must be divisible by this value. |
reduce.mcmc |
The number of reduced MCMC iterations for marginal likelihood computations.
If |
degree.normal |
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." |
UL.Normal |
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." |
DIC |
If |
Waic |
If |
marginal |
If |
plotUU |
If |
plotZ |
If |
constant |
If |
b0 |
The prior mean of |
B0 |
The prior variance of |
c0 |
= 0.1 |
d0 |
= 0.1 |
u0 |
|
u1 |
|
v0 |
|
v1 |
|
a |
|
b |
|
Value
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
References
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.
See Also
Examples
## Not run:
set.seed(1973)
\## Generate an array (30 by 30 by 40) with block transitions
from 2 blocks to 3 blocks
Y <- MakeBlockNetworkChange(n=10, T=40, type ="split")
G <- 100 ## Small mcmc scans to save time
\## Fit multiple models for break number detection using Bayesian model comparison
out0 <- NetworkStatic(Y, R=2, mcmc=G, burnin=G, verbose=G, Waic=TRUE)
out1 <- NetworkChange(Y, R=2, m=1, mcmc=G, burnin=G, verbose=G, Waic=TRUE)
out2 <- NetworkChange(Y, R=2, m=2, mcmc=G, burnin=G, verbose=G, Waic=TRUE)
out3 <- NetworkChange(Y, R=2, m=3, mcmc=G, burnin=G, verbose=G, Waic=TRUE)
outlist <- list(out0, out1, out2, out3)
\## The most probable model given break number 0 to 3 and data is out1 according to WAIC
WaicCompare(outlist)
plotU(out1)
plotV(out1)
## End(Not run)