MCMClogit {MCMCpack} | R Documentation

## Markov Chain Monte Carlo for Logistic Regression

### Description

This function generates a sample from the posterior distribution of a logistic regression model using a random walk Metropolis algorithm. 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

    MCMClogit(
      formula,
      data = NULL,
      burnin = 1000,
      mcmc = 10000,
      thin = 1,
      tune = 1.1,
      verbose = 0,
      seed = NA,
      beta.start = NA,
      b0 = 0,
      B0 = 0,
      user.prior.density = NULL,
      logfun = TRUE,
      marginal.likelihood = c("none", "Laplace"),
      ...
    )

### Arguments

`formula` | Model formula.
`data` | Data frame.
`burnin` | The number of burn-in iterations for the sampler.
`mcmc` | The number of Metropolis iterations for the sampler.
`thin` | The thinning interval used in the simulation. The number of mcmc iterations must be divisible by this value.
`tune` | Metropolis tuning parameter. Can be either a positive scalar or a k-vector, where k is the length of β.Make sure that the acceptance rate is satisfactory (typically between 0.20 and 0.5) before using the posterior sample for inference.
`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 current beta vector, and the Metropolis 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 the maximum likelihood estimate of β as the starting value.
`b0` | If `user.prior.density==NULL` `b0` is the prior mean of β under a multivariate normal prior. 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` | If `user.prior.density==NULL` `B0` is the prior precision of β under a multivariate normal prior. 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 for beta.
`user.prior.density` | If non-NULL, the prior (log)density up to a constant of proportionality. This must be a function defined in R whose first argument is a continuous (possibly vector) variable. This first argument is the point in the state space at which the prior (log)density is to be evaluated. Additional arguments can be passed to `user.prior.density()` by inserting them in the call to `MCMClogit()`. See the Details section and the examples below for more information.
`logfun` | Logical indicating whether `use.prior.density()` returns the natural log of a density function (TRUE) or a density (FALSE).
`marginal.likelihood` | How should the marginal likelihood be calculated? Options are: `none` in which case the marginal likelihood will not be calculated or `Laplace` in which case the Laplace approximation (see Kass and Raftery, 1995) is used.
`...` | further arguments to be passed

### Details

`MCMClogit` simulates from the posterior distribution of a logistic regression model using a random walk Metropolis algorithm. 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 model takes the following form:

y_i \sim \mathcal{B}ernoulli(π_i)

Where the inverse link function:

π_i = \frac{\exp(x_i'β)}{1 + \exp(x_i'β)}

By default, we assume a multivariate Normal prior on β:

β \sim \mathcal{N}(b_0,B_0^{-1})

Additionally, arbitrary user-defined priors can be specified with the `user.prior.density` argument.

If the default multivariate normal prior is used, the Metropolis proposal distribution is centered at the current value of β and has variance-covariance V = T (B_0 + C^{-1})^{-1} T , where T is a the diagonal positive definite matrix formed from the `tune`, B_0 is the prior precision, and C is the large sample variance-covariance matrix of the MLEs. This last calculation is done via an initial call to `glm`.

If a user-defined prior is used, the Metropolis proposal distribution is centered at the current value of β and has variance-covariance V = T C T, where T is a the diagonal positive definite matrix formed from the `tune` and C is the large sample variance-covariance matrix of the MLEs. This last calculation is done via an initial call to `glm`.

### Value

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

### References

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/.

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`, `glm`

### Examples

       ## Not run:
       ## default improper uniform prior
       data(birthwt)
       posterior <- MCMClogit(low~age+as.factor(race)+smoke, data=birthwt)
       plot(posterior)
       summary(posterior)

       ## multivariate normal prior
       data(birthwt)
       posterior <- MCMClogit(low~age+as.factor(race)+smoke, b0=0, B0=.001,
                              data=birthwt)
       plot(posterior)
       summary(posterior)

       ## user-defined independent Cauchy prior
       logpriorfun <- function(beta){
         sum(dcauchy(beta, log=TRUE))
       }

       posterior <- MCMClogit(low~age+as.factor(race)+smoke,
                              data=birthwt, user.prior.density=logpriorfun,
                              logfun=TRUE)
       plot(posterior)
       summary(posterior)

       ## user-defined independent Cauchy prior with additional args
       logpriorfun <- function(beta, location, scale){
         sum(dcauchy(beta, location, scale, log=TRUE))
       }

       posterior <- MCMClogit(low~age+as.factor(race)+smoke,
                              data=birthwt, user.prior.density=logpriorfun,
                              logfun=TRUE, location=0, scale=10)
       plot(posterior)
       summary(posterior)

    ## End(Not run)

[Package MCMCpack version 1.5-0 Index]