MCMCoprobit {MCMCpack} | R Documentation

## Markov Chain Monte Carlo for Ordered Probit Regression

### Description

This function generates a sample from the posterior distribution of an ordered probit regression model using the data augmentation approach of Albert and Chib (1993), with cut-points sampled according to Cowles (1996) or Albert and Chib (2001). 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

    MCMCoprobit(
      formula,
      data = parent.frame(),
      burnin = 1000,
      mcmc = 10000,
      thin = 1,
      tune = NA,
      tdf = 1,
      verbose = 0,
      seed = NA,
      beta.start = NA,
      b0 = 0,
      B0 = 0,
      a0 = 0,
      A0 = 0,
      mcmc.method = c("Cowles", "AC"),
      ...
    )

### Arguments

`formula` | Model formula.
`data` | Data frame.
`burnin` | The number of burn-in iterations for the sampler.
`mcmc` | The number of MCMC iterations for the sampler.
`thin` | The thinning interval used in the simulation. The number of Gibbs iterations must be divisible by this value.
`tune` | The tuning parameter for the Metropolis-Hastings step. Default of NA corresponds to a choice of 0.05 divided by the number of categories in the response variable.
`tdf` | Degrees of freedom for the multivariate-t proposal distribution when `mcmc.method` is set to "IndMH". Must be positive.
`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 Metropolis-Hastings acceptance rate 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 value for the β vector. 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 starting value for all of the betas. The default value of NA will use rescaled estimates from an ordered logit model.
`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 β. Default value of 0 is equivalent to an improper uniform prior on β.
`a0` | The prior mean of γ. 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.
`A0` | The prior precision of γ. 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 γ. Default value of 0 is equivalent to an improper uniform prior on γ.
`mcmc.method` | Can be set to either "Cowles" (default) or "AC" to perform posterior sampling of cutpoints based on Cowles (1996) or Albert and Chib (2001) respectively.
`...` | further arguments to be passed

### Details

`MCMCoprobit` simulates from the posterior distribution of a ordered probit regression model using data augmentation. The simulation proper is done in compiled C++ code to maximize efficiency. Please consult the coda documentation for a comprehensive list of functions that can be used to analyze the posterior sample.

The observed variable y_i is ordinal with a total of C categories, with distribution governed by a latent variable:

z_i = x_i'β + \varepsilon_i

The errors are assumed to be from a standard Normal distribution. The probabilities of observing each outcome is governed by this latent variable and C-1 estimable cutpoints, which are denoted γ_c. The probability that individual i is in category c is computed by:

π_{ic} = Φ(γ_c - x_i'β) - Φ(γ_{c-1} - x_i'β)

These probabilities are used to form the multinomial distribution that defines the likelihoods.

`MCMCoprobit` provides two ways to sample the cutpoints. Cowles (1996) proposes a sampling scheme that groups sampling of a latent variable with cutpoints. In this case, for identification the first element γ_1 is normalized to zero. Albert and Chib (2001) show that we can sample cutpoints indirectly without constraints by transforming cutpoints into real-valued parameters (α).

### Value

An mcmc object that contains the posterior sample. This object can be summarized by functions provided by the coda package.

### References

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

M. K. Cowles. 1996. “Accelerating Monte Carlo Markov Chain Convergence for Cumulative-link Generalized Linear Models." Statistics and Computing. 6: 101-110.

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/.

Valen E. Johnson and James H. Albert. 1999. Ordinal Data Modeling. Springer: New York.

Albert, James and Siddhartha Chib. 2001. “Sequential Ordinal Modeling with Applications to Survival Data." Biometrics. 57: 829-836.

Daniel Pemstein, Kevin M. Quinn, and Andrew D. Martin. 2007. Scythe Statistical Library 1.0. http://scythe.lsa.umich.edu.

Martyn Plummer, Nicky Best, Kate Cowles, and Karen Vines. 2006. “Output Analysis and Diagnostics for MCMC (CODA)”, R News. 6(1): 7-11. https://CRAN.R-project.org/doc/Rnews/Rnews_2006-1.pdf.

### See Also

`plot.mcmc`,`summary.mcmc`

### Examples

       ## Not run:
       x1 <- rnorm(100); x2 <- rnorm(100);
       z <- 1.0 + x1*0.1 - x2*0.5 + rnorm(100);
       y <- z; y[z < 0] <- 0; y[z >= 0 & z < 1] <- 1;
       y[z >= 1 & z < 1.5] <- 2; y[z >= 1.5] <- 3;
       out1 <- MCMCoprobit(y ~ x1 + x2, tune=0.3)
       out2 <- MCMCoprobit(y ~ x1 + x2, tune=0.3, tdf=3, verbose=1000, mcmc.method="AC")
       summary(out1)
       summary(out2)
       plot(out1)
       plot(out2)

    ## End(Not run)

[Package MCMCpack version 1.5-0 Index]