# 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

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```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

`fisher.test`

## Examples

 ```1 2 3 4 5 6 7``` ```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 29, 2017, 12:37 p.m.