Description Usage Arguments Value See Also Examples
Runs BAnOCC to fit the model and generate appropriate convergence metrics and inference.
1 2 3 4 
compiled_banocc_model 
The compiled stan model (as with

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 
L 
The prior variancecovariance for m (must be
positivedefinite with dimension PxP where P=number of features/columns
in 
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 
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 stepsize 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 
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

control 
A named 
verbose 
Print informative statements as the function executes? 
num_level 
The number of indentations to add to the output when

Returns a named list with the following elements:
The data formatted as a named list that includes the
input data (C
) and the prior parameters (n
, L
,
a
, b
)
The stanfit
object returned by the call to
sampling
vignette("banoccvignette")
for more examples.
1 2 3 4 5 6 7  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)

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