Sample from Hierarchical Model with given Row and Column Sums

1 2 3 4 5 | ```
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,
tol = .Machine$double.eps^0.25)
``` |

`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 |

`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). |

`tol` |
tolerance used in checks for equality. Defaults to |

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
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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