NetworkChange (version 0.8)

BreakDiagnostic: Detect a break number using different metrics

Description

Detect a break number using different metrics

Usage

BreakDiagnostic(
  Y,
  R = 2,
  mcmc = 100,
  burnin = 100,
  verbose = 100,
  thin = 1,
  UL.Normal = "Orthonormal",
  v0 = NULL,
  v1 = NULL,
  break.upper = 3,
  a = 1,
  b = 1
)

Arguments

Y

Reponse tensor

R

Dimension of latent space. The default is 2.

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 verbose is greater than 0 the iteration number, the \(\beta\) vector, and the error variance are printed to the screen every verboseth iteration.

thin

The thinning interval used in the simulation. The number of MCMC iterations must be divisible by this value.

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."

v0

\(v_0/2\) is the shape parameter for the inverse Gamma prior on variance parameters for V. If v0 = NULL, a value is computed from a test run of NetworkStatic.

v1

\(v_1/2\) is the scale parameter for the inverse Gamma prior on variance parameters for V. If v1 = NULL, a value is computed from a test run of NetworkStatic.

break.upper

Upper threshold for break number detection. The default is break.upper = 3.

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.

References

Jong Hee Park and Yunkyun Sohn. 2020. "Detecting Structural Change in Longitudinal Network Data." Bayesian Analysis. Vol.15, No.1, pp.133-157.

Examples

# NOT RUN {
   
# }
# NOT RUN {
   set.seed(19333)
   ## Generate an array (15 by 15 by 20) with a block merging transition
   Y <- MakeBlockNetworkChange(n=5, T=20, type ="merge")

   ## Fit 3 models (no break, one break, and two break) for break number detection 
   detect <- BreakDiagnostic(Y, R=2, break.upper = 2)
   
   ## Look at the graph
   detect[[1]]; print(detect[[2]])

# }
# NOT RUN {


# }