| 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)