Sets up a flash object with no factors. Since all other
flash_xxx functions take a flash or flash_fit object
as their first argument, calling flash_init should be the first step
in any flash pipeline. See flash for examples of usage.
flash_init(data, S = NULL, var_type = 0L, S_dim = NULL)The observations. Usually a matrix, but can also be a sparse
matrix of class Matrix or a low-rank matrix
representation as returned by, for example, svd,
irlba, rsvd, or
softImpute (in general, any list that
includes fields u, d, and v will be interpreted
as a low-rank matrix representation).
The standard errors. Can be NULL (in which case all residual
variance will be estimated) or a matrix, vector, or scalar. S
should be a scalar if standard errors are identical across observations. It
should be a vector if standard errors either vary across columns but are
constant within any given row, or vary across rows but are constant within
any given column (flash will use the length of the vector
to determine whether the supplied values correspond to rows or columns; if the
data matrix is square, then the sense must be specified using parameter
S_dim in function flash_init).
Describes the structure of the estimated residual variance.
Can be NULL, 0, 1, 2, or c(1, 2). If
NULL, then S accounts for all residual variance. If
var_type = 0, then the estimated residual variance (which is added
to any variance given by S) is assumed to be constant
across all observations. Setting var_type = 1 estimates a single
variance parameter for each row; var_type = 2 estimates one
parameter for each column; and var_type = c(1, 2) optimizes over
all rank-one matrices (that is, it assumes that the residual variance
parameter \(s_{ij}\) can be written \(s_{ij} = a_i b_j\), where the
\(n\)-vector \(a\) and the \(p\)-vector \(b\) are to be
estimated).
Note that if any portion of the residual variance is to be estimated, then
it is usually faster to set S = NULL and to let flash
estimate all of the residual variance. Further, var_type = c(1, 2)
is typically much slower than other options, so it should be used with
care.
If the argument to S is a vector and the data matrix is
square, then S_dim must specify whether S encodes row-wise or
column-wise standard errors. More precisely,
if S_dim = 1, then S will be interpreted as giving
standard errors that vary across rows but are constant within any particular
row; if S_dim = 2, then it will be interpreted as giving
standard errors that vary across columns but are constant within any
particular column. If S is a matrix or scalar, or if the data
matrix is not square, then S_dim should be left unspecified
(NULL).
An initialized flash object (with no factors).