simulateConditional: Simulates from the conditional distribution of a log-linear...

In exactLoglinTest: Monte Carlo Exact Tests for Log-linear models

Description

Simulates from the conditional distribution of log-linear models given the sufficient statistics.

Usage

simulateConditional(formula,
                     data,
                     dens = hyper,
                     nosim = 10^3,
                     method = "bab",
                     tdf = 3,
                     maxiter = nosim,
                     p = NULL,
                     y.start = NULL)
simtable.bab(args, nosim = NULL, maxiter = NULL)
simtable.cab(args, nosim = NULL, p = NULL, y.start = NULL)

Arguments

formula	A formula for the log-linear model
data	A data frame
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".
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".
y.start	An optional starting value when method = "cab"
args	An object of class "bab" or "cab"

Value

A matrix where each simulated table is a row.

Author(s)

Brian Caffo

See Also

fisher.test

Examples

data(czech.dat)
chain2 <- simulateConditional(y ~ (A + B + C + D + E + F) ^ 2,
                               data = czech.dat,
                               method = "cab",
                               nosim = 10 ^ 3,
                               p = .4,
                               dens = function(y) 0)

Example output

exactLoglinTest documentation built on May 1, 2019, 9:58 p.m.