.fitMCMC {MIMOSA} | R Documentation

## Fit the MIMOSA model via MCMC

### Description

This is an internal function that fits the MIMOSA model via MCMC. It is called from `MIMOSA`

### Usage

    .fitMCMC(
      data,
      inits = NULL,
      iter = 250000,
      burn = 50000,
      thin = 1,
      tune = 100,
      outfile = basename(tempfile(tmpdir = ".", fileext = ".dat")),
      alternative = "greater",
      UPPER = 0.5,
      LOWER = 0.15,
      FAST = TRUE,
      EXPRATE = 1e-04,
      pXi = c(1, 1),
      seed = 10
    )

### Arguments

`data` | a `list` with elements names 'n.stim' and 'n.unstim', the stimulated and unstimulated counts. Must be at least of dimension 2.
`inits` | the initialization parameters for the MCMC routine. Can be initialized from `MDMix` with `initonly=TRUE`.
`iter` | the number of Mote Carlo iterations
`burn` | the number of burn-in iterations
`thin` | The thinning interval
`tune` | the number of iterations used for tuning the step size
`outfile` | the output file name
`alternative` | either 'greater' or 'not equal' for fitting the one-sided or two-sided MIMOSA model, respectively.
`UPPER` | tuning parameter for the upper bound on the acceptance ratio of each paramter
`LOWER` | tuning parmeter for the lower bound on the acceptance ratio of each paramter
`FAST` | `TRUE,FALSE`. Use the heuristic (FAST=TRUE) for fitting a one-sided model rather than recomputing the normalization constant via MCMC for each step. @importFrom coda mcmc
`EXPRATE` | the mean of the prior distribution for the model hyperparameters.
`pXi` | is the prior on the w, beta(1,1) by default).
`seed` | `numeric` random seed @rdname fitMCMC @name .fitMCMC @importFrom data.table fread

[Package MIMOSA version 1.32.0 Index]