simulateConditional {exactLoglinTest} | R Documentation |
Simulates from the conditional distribution of log-linear models given the sufficient statistics.
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)
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" |
A matrix where each simulated table is a row.
Brian Caffo
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)