flash_add_fixed_loadings: Add a set of fixed loadings to a flash fit object
In stephenslab/flashr2: Empirical Bayes Matrix Factorization

flash_add_fixed_loadings

R Documentation

Add a set of fixed loadings to a flash fit object

Description

Add a set of fixed loadings to a flash fit object

Usage

flash_add_fixed_loadings(data, LL, f_init = NULL, fixl = NULL,
  init_fn = "udv_si", backfit = TRUE, ...)

Arguments

`data`	An n by p matrix or a flash data object created using `flash_set_data`.
`LL`	The loadings, an n by K matrix. Missing values will be initialized by the mean of the relevant column (but will generally be re-estimated when refitting the model).
`f_init`	The flash object or flash fit object to which new factors are to be added. If `f_init = NULL`, then a new flash object is created.
`fixl`	An n by K matrix of `TRUE`/`FALSE` values indicating which elements of `LL` should be considered fixed and not changed during updates. The default is to fix all non-missing values, so missing values will be updated when the flash object is updated.
`init_fn`	The function used to initialize factors. Options include: `"udv_si"` Provides a simple wrapper to `softImpute` to provide a rank-one initialization. Uses option `type = "als"`. `"udv_si_svd"` Uses `softImpute` with option `type = "svd"`. `"udv_svd"` Provides a simple wrapper to `svd`. `"udv_random"` Provides a random initialization of factors. A user-specified function can also be used. This function should take parameters `(Y, K)`, where `Y` is an n by p matrix of data (or a flash data object) and `K` is the number of factors. It should output a list with elements `(u, d, v)`, where `u` is a n by K matrix, `v` is a p by K matrix, and `d` is a K vector. (If the input data includes missing values, then the function must be able to deal with missing values in its input matrix.)
`backfit`	If `TRUE`, factors are refined via the backfitting algorithm.
`...`	Additional parameters to be passed to `flash_backfit`. Note that `nullcheck` defaults to `FALSE` here.