getDefaultOpts: Get default options for BIBFA
In CCAGFA: Bayesian Canonical Correlation Analysis and Group Factor Analysis

Description Usage Details Value Author(s) See Also Examples

A helper function that creates a list of options to be passed for CCA and GFA.

1	getDefaultOpts()

To run the code with other option values, first run this function and then directly modify the entries before passing the list to CCA and GFA.

`R`	The rank of hierarhical low-rank ARD prior. Possible values are all integers, including zero, and "full". When R equals "full" or R equals or is larger than the minimum value of the number of data sets and the number of latent factors, that is min(M,K), the prior corresponds to ARD prior with no low-rank structure.
`lambda`	The regularization parameter of the low-rank ARD model.
`rotate`	Whether to optimize for a linear transformation to make the variational updates less correlated.
`init.tau`	Initial values for the noise precision.
`iter.crit`	The iteration is terminated when the relative change in the lower bound for the marginal likelihood drops below this threshold.
`iter.max`	Maximum number of iterations.
`opt.method`	Which method to use for optimizing the rotation; "BFGS" or "L-BFGS".
`lbfgs.factr`	Optimization parameter of L-BFGS.
`bfgs.crit`	Optimization parameter of BFGS.
`opt.iter`	Number of iterations for the (L-)BFGS optimization.
`addednoise`	A small constant used to de-correlate latent variables of inactive components.
`prior.alpha_0`	Gamma prior for ARD.
`prior.beta_0`	Gamma prior for ARD.
`prior.alpha_0t`	Gamma prior for tau.
`prior.beta_0t`	Gamma prior for tau.
`dropK`	Whether to prune out empty factors from the model during inference.
`low.mem`	Whether to store and return the covariance matrices of W.
`verbose`	The amount of details printed while running `CCA` and `GFA`. 0=none, 1=medium, 2=high.

Seppo Virtanen, Eemeli Leppaaho and Arto Klami

CCA,GFA.

 # opts <- getDefaultOpts()  # Get the default options
 # opts$verbose <- 1         # Change some of them
 # opts$init.tau <- 10^5

 # Run the model with the new options
 # model <- CCAexperiment(Y,K,opts)