BreakDiagnostic function

Detect a break number using different metrics

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.

Examples

## 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]])
## End(Not run)

References

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