View source: R/NetworkChange.r
NetworkChange | R Documentation |
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 )
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 β. This must be a scalar. The default value is 0. |
B0 |
The prior variance of β. This must be a scalar. The default value is 1. |
c0 |
= 0.1 |
d0 |
= 0.1 |
u0 |
u_0/2 is the shape parameter for the inverse Gamma prior on variance parameters for U. The default is 10. |
u1 |
u_1/2 is the scale parameter for the inverse Gamma prior on variance parameters for U. The default is 1. |
v0 |
v_0/2 is the shape parameter for the inverse Gamma prior on variance parameters for V. The default is 10. |
v1 |
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 |
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 |
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. |
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
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
## 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)
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