run_banocc: Runs BAnOCC to fit the model and generate appropriate...
In banocc: Bayesian ANalysis Of Compositional Covariance

Description Usage Arguments Value See Also Examples

Runs BAnOCC to fit the model and generate appropriate convergence metrics and inference.

run_banocc(compiled_banocc_model, C, n = rep(0, ncol(C)), L = 10 *
  diag(ncol(C)), a = 0.5, b = 0.01, cores = getOption("mc.cores", 1L),
  chains = 4, iter = 50, warmup = floor(iter/2), thin = 1,
  init = NULL, control = NULL, verbose = FALSE, num_level = 0)

`compiled_banocc_model`	The compiled stan model (as with `stan_model(model_code = banocc_model)`).
`C`	The dataset as a data frame or matrix. This should be N by P with N samples as the rows and P features as the columns.
`n`	The prior mean for m; vectors of length less than P (the number of features/columns of `C`) will be recycled.
`L`	The prior variance-covariance for m (must be positive-definite with dimension PxP where P=number of features/columns in `C`), or a vector of length p of variances for m. If a vector of length less than P is given, it will be recycled.
`a`	The shape parameter of a gamma distribution (the prior on the shrinkage parameter lambda)
`b`	The rate parameter of a gamma distribution (the prior on the shrinkage parameter lambda)
`cores`	Number of cores to use when executing the chains in parallel, which defaults to 1 but we recommend setting the `mc.cores` option to be as many processors as the hardware and RAM allow (up to the number of chains).
`chains`	A positive integer specifying the number of Markov chains. The default is 4.
`iter`	A positive integer specifying the number of iterations for each chain (including warmup). The default is 2000.
`warmup`	A positive integer specifying the number of warmup (aka burnin) iterations per chain. If step-size adaptation is on (which it is by default), this also controls the number of iterations for which adaptation is run (and hence these warmup samples should not be used for inference). The number of warmup iterations should not be larger than `iter` and the default is `iter/2`.
`thin`	A positive integer specifying the period for saving samples. The default is 1, which is usually the recommended value.
`init`	The initial values as a list (see `sampling` in the `rstan` package). Default value is NULL, which means that initial values are sampled from the priors for parameters m and lambda while O is set to the identity matrix.
`control`	A named `list` of parameters to control the sampler's behavior. See the details in the documentation for the `control` argument in `stan`.
`verbose`	Print informative statements as the function executes?
`num_level`	The number of indentations to add to the output when `verbose = TRUE`.

Returns a named list with the following elements:

Data: The data formatted as a named list that includes the input data (C) and the prior parameters (n, L, a, b)
Fit: The stanfit object returned by the call to sampling

vignette("banocc-vignette") for more examples.

  data(compositions_null)
  ## Not run: 
    compiled_banocc_model <- rstan::stan_model(model_code=banocc_model)
    b_stanfit <- run_banocc(C=compositions_null,
                            compiled_banocc_model=compiled_banocc_model)
  
## End(Not run)