dcFit: Internal function for iterative model fitting with data...
In dclone: Data Cloning and MCMC Tools for Maximum Likelihood Methods

Description Usage Arguments Value Author(s) See Also

This is the workhorse for dc.fit and dc.parfit.

.dcFit(data, params, model, inits, n.clones, 
    multiply = NULL, unchanged = NULL, 
    update = NULL, updatefun = NULL, initsfun = NULL, 
    flavour = c("jags", "bugs"), 
    n.chains=3, cl = NULL, parchains = FALSE, ...)

`data`	A named list (or environment) containing the data.
`params`	Character vector of parameters to be sampled. It can be a list of 2 vectors, 1st element is used as parameters to monitor, the 2nd is used as parameters to use in calculating the data cloning diagnostics.
`model`	Character string (name of the model file), a function containing the model, or a `custommodel` object (see Examples).
`inits`	Optional specification of initial values in the form of a list or a function (see Initialization at `jags.model`). If missing, will be treated as `NULL` and initial values will be generated automatically.
`n.clones`	An integer vector containing the numbers of clones to use iteratively.
`multiply`	Numeric or character index for list element(s) in the `data` argument to be multiplied by the number of clones instead of repetitions.
`unchanged`	Numeric or character index for list element(s) in the `data` argument to be left unchanged.
`update`	Numeric or character index for list element(s) in the `data` argument that has to be updated by `updatefun` in each iterations. This usually is for making priors more informative, and enhancing convergence. See Details and Examples.
`updatefun`	A function to use for updating `data[[update]]`. It should take an 'mcmc.list' object as 1st argument, 2nd argument can be the number of clones. See Details and Examples.
`initsfun`	A function to use for generating initial values, `inits` are updated by the object returned by this function from the second iteration. If initial values are not dependent on the previous iteration, this should be `NULL`, otherwise, it should take an 'mcmc.list' object as 1st argument, 2nd argument can be the number of clones. This feature is useful if latent nodes are provided in `inits` so it also requires to be cloned for subsequent iterations. See Details and Examples.
`flavour`	If `"jags"`, the function `jags.fit` is called. If `"bugs"`, the function `bugs.fit` is called.
`n.chains`	Number of chains to generate.
`cl`	A cluster object created by `makeCluster`, or an integer, see `parDosa` and `evalParallelArgument`.
`parchains`	Logical, whether parallel chanins should be run.
`...`	Other values supplied to `jags.fit`, or `bugs.fit`, depending on the `flavour` argument.