pois_cov_ed: Estimate Prior Covariance Matrices using Extreme...
In stephenslab/poisson.mash.alpha: Poisson Multivariate Adaptive Shrinkage

pois_cov_ed

R Documentation

Estimate Prior Covariance Matrices using Extreme Deconvolution

Description

Perform extreme deconvolution (ED) to estimate data-driven prior covariance matrices.

Usage

pois_cov_ed(
  data,
  subset,
  Ulist,
  ulist,
  ulist.dd,
  ruv = FALSE,
  Fuv,
  update.mu = TRUE,
  verbose = FALSE,
  init = list(),
  control = list()
)

pois_cov_ed_control_default()

Arguments

`data`	“pois.mash” data object, typically created by calling `pois_mash_set_data`.
`subset`	The indices of features to be used. Defaults to using all features.
`Ulist`	A list of H full-rank covariance matrices (e.g., initialized by `pois_cov_init`).
`ulist`	A list of G numeric vectors each of which defines a rank-1 covariance matrix.
`ulist.dd`	Logical vector of length G denoting whether each element in `ulist` is data-driven (`TRUE`) or canonical (`FALSE`). Defaults to data-driven for all elements. For canonical covariances, the spanned space is not updated.
`ruv`	Logical scalar indicating whether to account for unwanted variation. If `ruv = TRUE`, `Fuv` must be provided.
`Fuv`	J x D matrix of latent factors causing unwanted variation, with features as rows and latent factors as columns.
`update.mu`	A logical scalar indicating whether to update gene-specific means mu. If `update.mu = FALSE`, initial mu must be provided in `init`.
`verbose`	Logical scalar indicating whether to print ELBO at each iteration.
`init`	Optional list of initial values for model parameters (e.g., returned by `pois_mash_prefit`).
`control`	A list of control parameters with the following elements: “maxiter”, maximum number of ED iterations; “maxiter.q”, maximum number of inner loop iterations to update variational parameters at each ED iteration; “maxpsi2”, maximum for the gene-specific dispersion parameter `psi2`; “maxbias”, maximum for the gene-specific range of bias caused by unwanted variation; “tol.stop”, tolerance for assessing convergence of ED, as measured by absolute change in ELBO; “tol.q”, relative tolerance for assessing convergence of variational parameters at each ED iteration; and “tol.rho”tolerance for assessing convergence of effects corresponding to unwanted variation. Any named components will override the default optimization algorithm settings (as they are defined by `pois_cov_ed_control_default`).

Value

A list including the following elements:

`Ulist`	List of H full-rank covariance matrices.
`ulist`	List of G numeric vectors each of which forms a rank-1 covariance matrix.
`pi`	Numeric vector of length H + G containing the mixture proportions for Ulist and ulist.