mcexact: Computes Monte Carlo exact P-values for general log-linear...
In exactLoglinTest: Monte Carlo Exact Tests for Log-linear models

Description Usage Arguments Value Author(s) References See Also Examples

This function computes Monte Carlo estimates of conditional P-values for goodness of fit tests for general log-linear models.

mcexact(formula,
        data,
        stat = gof,
        dens = hyper,
        nosim = 10 ^ 3,
        method = "bab",
        savechain = FALSE,
        tdf = 3,
        maxiter = nosim,
        p = NULL,
        batchsize = NULL)

build.mcx.obj(formula,
        data,
        stat = gof,
        dens = hyper,
        nosim = 10 ^ 3,
        method = "bab",
        savechain = FALSE,
        tdf = 3,
        maxiter = nosim,
        p = NULL,
        batchsize = NULL)

`formula`	Null model formula specified as in `glm`
`data`	Data frame
`stat`	The test statistic, a function of the form `function(y, mu.hat)` where `y` is the observed and `mu.hat` are the fitted values. Current default `gof` is a bivariate function of the deviance and the Pearson chi-squared.
`dens`	The target density on the log scale up to a constant of proportionallity. A function of the form `function(y)`. Current default is (proportional to) the log of the generalized hypergeometric density.
`nosim`	Desired number of simulations.
`method`	Possibly two values, the importance sampling method of Booth and Butler, `method = "bab"` or the MCMC approach of Caffo and Booth `method = "cab"`.
`savechain`	If `TRUE` saves the values of the chain.
`tdf`	A tuning parameter
`maxiter`	For `method = "bab"` number of iterations is different from the number of simulations. `maxiter` is a bound on the total number of iterations.
`p`	A tuning parameter for `method = "cab"`.
`batchsize`	Required batchsizes for `method = "cab"`.

Returns a list of class either "bab" or "cab" depending on method. The list contains all of the inputs plus all required information to resume the simulation. Generic functions print and summary format the output while update can be used to resume simulations. mcexact is the front end while build.mcx.obj simply builds the basic object that mcexact applies to. simulate.conditional generates a matrix of simulated tables.

Brian Caffo

Booth and Butler (1999), "An importance sampling algorithm for exact conditional tests in log-linear models", Biometrika 86: 321-332.

Caffo and Booth (2001). "A Markov Chain Monte Carlo Algorithm for Approximating Exact Conditional Probabilities", The Journal of Computational and Graphical Statistics 10: 730-45.

http://www.biostat.jhsph.edu/~bcaffo/downloads.htm

fisher.test

#library(mcexact)
set.seed(1)

#importance sampling
data(residence.dat)
mcx <- mcexact(y ~ res.1985 + res.1980 + factor(sym.pair), data = residence.dat) 
summary(mcx)

#mcmc
data(pathologist.dat)
mcx <- mcexact(y ~ factor(A) + factor(B) + I(A * B),
               data = pathologist.dat,
               method = "cab",
               p = .5,
               nosim = 10 ^ 4,
               batchsize = 100)
summary(mcx)