MCMCprobitChange {MCMCpack} | R Documentation

## Markov Chain Monte Carlo for a linear Gaussian Multiple Changepoint Model

### Description

This function generates a sample from the posterior distribution of a linear Gaussian model with multiple changepoints. The function uses the Markov chain Monte Carlo method of Chib (1998). The user supplies data and priors, and a sample from the posterior distribution is returned as an mcmc object, which can be subsequently analyzed with functions provided in the coda package.

### Usage

    MCMCprobitChange(
      formula,
      data = parent.frame(),
      m = 1,
      burnin = 10000,
      mcmc = 10000,
      thin = 1,
      verbose = 0,
      seed = NA,
      beta.start = NA,
      P.start = NA,
      b0 = NULL,
      B0 = NULL,
      a = NULL,
      b = NULL,
      marginal.likelihood = c("none", "Chib95"),
      ...
    )

### Arguments

`formula` | Model formula.
`data` | Data frame.
`m` | The number of changepoints.
`burnin` | The number of burn-in iterations for the sampler.
`mcmc` | The number of MCMC iterations after burnin.
`thin` | The thinning interval used in the simulation. The number of MCMC iterations must be divisible by this value.
`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 β vector, and the error variance are printed to the screen every `verbose`th iteration.
`seed` | The seed for the random number generator. If NA, the Mersenne Twister generator is used with default seed 12345; if an integer is passed it is used to seed the Mersenne twister. The user can also pass a list of length two to use the L'Ecuyer random number generator, which is suitable for parallel computation. The first element of the list is the L'Ecuyer seed, which is a vector of length six or NA (if NA a default seed of `rep(12345,6)` is used). The second element of list is a positive substream number. See the MCMCpack specification for more details.
`beta.start` | The starting values for the β vector. This can either be a scalar or a column vector with dimension equal to the number of betas. The default value of of NA will use the MLE estimate of β as the starting value. If this is a scalar, that value will serve as the starting value mean for all of the betas.
`P.start` | The starting values for the transition matrix. A user should provide a square matrix with dimension equal to the number of states. By default, draws from the `Beta(0.9, 0.1)` are used to construct a proper transition matrix for each raw except the last raw.
`b0` | The prior mean of β. This can either be a scalar or a column vector with dimension equal to the number of betas. If this takes a scalar value, then that value will serve as the prior mean for all of the betas.
`B0` | The prior precision of β. This can either be a scalar or a square matrix with dimensions equal to the number of betas. If this takes a scalar value, then that value times an identity matrix serves as the prior precision of beta. Default value of 0 is equivalent to an improper uniform prior for beta.
`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.
`marginal.likelihood` | How should the marginal likelihood be calculated? Options are: `none` in which case the marginal likelihood will not be calculated, and `Chib95` in which case the method of Chib (1995) is used.
`...` | further arguments to be passed

### Details

`MCMCprobitChange` simulates from the posterior distribution of a probit regression model with multiple parameter breaks. The simulation is based on Chib (1998) and Park (2011).

The model takes the following form:

\Pr(y_t = 1) = Φ(x_i'β_m) \;\; m = 1, …, M

Where M is the number of states, and β_m is a parameter when a state is m at t.

We assume Gaussian distribution for prior of β:

β_m \sim \mathcal{N}(b_0,B_0^{-1}),\;\; m = 1, …, M

And:

p_{mm} \sim \mathcal{B}eta(a, b),\;\; m = 1, …, M

Where M is the number of states.

### 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 `prob.state` storage matrix that contains the probability of state_i for each period, the log-likelihood of the model (`loglike`), and the log-marginal likelihood of the model (`logmarglike`).

### References

Jong Hee Park. 2011. “Changepoint Analysis of Binary and Ordinal Probit Models: An Application to Bank Rate Policy Under the Interwar Gold Standard." Political Analysis. 19: 188-204. <doi:10.1093/pan/mpr007>

Andrew D. Martin, Kevin M. Quinn, and Jong Hee Park. 2011. “MCMCpack: Markov Chain Monte Carlo in R.”, Journal of Statistical Software. 42(9): 1-21. https://www.jstatsoft.org/v42/i09/.

Siddhartha Chib. 1998. “Estimation and comparison of multiple change-point models.” Journal of Econometrics. 86: 221-241.

Albert, J. H. and S. Chib. 1993. “Bayesian Analysis of Binary and Polychotomous Response Data.” J. Amer. Statist. Assoc. 88, 669-679

### See Also

`plotState`, `plotChangepoint`

### Examples

    ## Not run:
    set.seed(1973)
    x1 <- rnorm(300, 0, 1)
    true.beta <- c(-.5, .2, 1)
    true.alpha <- c(.1, -1., .2)
    X <- cbind(1, x1)

    ## set two true breaks at 100 and 200
    true.phi1 <- pnorm(true.alpha[1] + x1[1:100]*true.beta[1])
    true.phi2 <- pnorm(true.alpha[2] + x1[101:200]*true.beta[2])
    true.phi3 <-  pnorm(true.alpha[3] + x1[201:300]*true.beta[3])

    ## generate y
    y1 <- rbinom(100, 1, true.phi1)
    y2 <- rbinom(100, 1, true.phi2)
    y3 <- rbinom(100, 1, true.phi3)
    Y <- as.ts(c(y1, y2, y3))

    ## fit multiple models with a varying number of breaks
    out0 <- MCMCprobitChange(formula=Y~X-1, data=parent.frame(), m=0,
                             mcmc=1000, burnin=1000, thin=1, verbose=1000,
                             b0 = 0, B0 = 0.1, a = 1, b = 1,  marginal.likelihood = c("Chib95"))
    out1 <- MCMCprobitChange(formula=Y~X-1, data=parent.frame(), m=1,
                             mcmc=1000, burnin=1000, thin=1, verbose=1000,
                             b0 = 0, B0 = 0.1, a = 1, b = 1,  marginal.likelihood = c("Chib95"))
    out2 <- MCMCprobitChange(formula=Y~X-1, data=parent.frame(), m=2,
                             mcmc=1000, burnin=1000, thin=1, verbose=1000,
                             b0 = 0, B0 = 0.1, a = 1, b = 1,  marginal.likelihood = c("Chib95"))
    out3 <- MCMCprobitChange(formula=Y~X-1, data=parent.frame(), m=3,
                             mcmc=1000, burnin=1000, thin=1, verbose=1000,
                             b0 = 0, B0 = 0.1, a = 1, b = 1,  marginal.likelihood = c("Chib95"))

    ## find the most reasonable one
    BayesFactor(out0, out1, out2, out3)

    ## draw plots using the "right" model
    plotState(out2)
    plotChangepoint(out2)

    ## End(Not run)

[Package MCMCpack version 1.5-0 Index]