Sample from Hierarchical Model with given Row and Column Sums

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Description

Sample from Hierarchical Model with given Row and Column Sums

Usage

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sample_HierarchicalModel(l, a, L_fixed = NA, model, nsamples = 10000,
  thin = choosethin(l = l, a = a, L_fixed = L_fixed, model = model,
  matrpertheta = matrpertheta, silent = silent), burnin = NA,
  matrpertheta = length(l)^2, silent = FALSE)

Arguments

l

observed row sum

a

observerd column sum

L_fixed

Matrix containing known values of L, where NA signifies that an element is not known. If L_fixed equates to NA (the default) then no values are assumed to be known.

model

Underlying model for p and lambda.

nsamples

number of samples to return.

thin

how many updates of theta to perform before outputting a sample.

burnin

number of iterations for the burnin. Defaults to 5 of the steps in the sampling part.

matrpertheta

number of matrix updates per update of theta.

silent

(default FALSE) suppress all output (including progress bars).

Examples

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n <- 10
m <- Model.Indep.p.lambda(Model.p.BetaPrior(n),
                          Model.lambda.GammaPrior(n,scale=1e-1))
x <- genL(m)
l <- rowSums(x$L)
a <- colSums(x$L)
## Not run: 
res <- sample_HierarchicalModel(l,a,model=m)

## End(Not run)
# fixing one values
L_fixed <- matrix(NA,ncol=n,nrow=n)
L_fixed[1,2:5] <- x$L[1,2:5]
## Not run: 
res <- sample_HierarchicalModel(l,a,model=m,L_fixed=L_fixed,
                                nsamples=1e2)
sapply(res$L,function(x)x[1,2:5])

## End(Not run)

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