getDefaultOpts: A function for generating a default set of parameters for...
In tensorBF: Bayesian Tensor Factorization

Description Usage Arguments Details Value Examples

View source: R/tensorBF.R

getDefaultOpts returns the default choices for model parameters.

1	getDefaultOpts(method = "CP")

method

the factorization method for which options are required. Currently only "CP" (default) is supported.

This function returns options for defining the model's high-level structure (sparsity priors), the hyperparameters, and the uninformative priors. We recommend keeping these as provided.

A list with the following model options:

`ARDX`	TRUE: use elementwise ARD prior for X, resulting in sparse X's. FALSE: use guassian prior for a dense X (default).
`ARDW`	TRUE: use elementwise ARD prior for W, resulting in sparse W's (default). FALSE: use guassian prior for a dense W.
`ARDU`	TRUE: use elementwise ARD prior for U, resulting in sparse U's. FALSE: use guassian prior for a dense U (default).
`iter.burnin`	The number of burn-in samples (default 5000).
`iter.sampling`	The number of saved posterior samples (default 50).
`iter.thinning`	The thinning factor to use in saving posterior samples (default 10).
`prior.alpha_0t`	The shape parameter for residual noise (tau's) prior (default 1).
`prior.beta_0t`	The rate parameter for residual noise (tau's) prior (default 1).
`prior.alpha_0`	The shape parameter for the ARD precisions (default 1e-3).
`prior.beta_0`	The rate parameter for the ARD precisions (default 1e-3).
`prior.betaW1`	Bernoulli prior for component activiations, prior.betaW1 < prior.betaW2: sparsity inducing (default: 1).
`prior.betaW2`	Bernoulli prior for component activation, (default: 1).
`init.tau`	The initial value for noise precision (default 1e3).
`verbose`	The verbosity level. 0=no printing, 1=moderate printing, 2=maximal printing (default 1).
`checkConvergence`	Check for the convergence of the data reconstruction, based on the Geweke diagnostic (default TRUE).

#To run the algorithm with other values:
opts <- getDefaultOpts()
opts$ARDW <- FALSE #Switch off Feature-level Sparsity on W's
 ## Not run: res <- tensorBF(Y=Y,opts=opts)