PLMM prep: a function to run checks, eigendecomposition, and rotation prior to fitting a PLMM model

This is an internal function for plmm()

Usage

plmm_prep(
  std_X,
  std_X_n,
  std_X_p,
  centered_y,
  penalty_factor,
  K = NULL,
  eta = NULL,
  fbm_flag,
  trace = NULL,
  ...
)

std_X: Column standardized design matrix. May include clinical covariates and other non-SNP data.
std_X_n: The number of observations in std_X (integer)
std_X_p: The number of features in std_X (integer)
centered_y: Continuous outcome vector, centered.
penalty_factor: A multiplicative factor for the penalty applied to each coefficient.
K: Similarity matrix used to rotate the data. This should either be: (1) a known matrix that reflects the covariance of y, (2) an estimate (Default is \(\frac{1}{p}(XX^T)\)), or (3) a list with components s and U, as returned by a previous plmm() model fit on the same data.
eta: Optional argument to input a specific eta term rather than estimate it from the data. If K is a known covariance matrix that is full rank, this should be 1.
fbm_flag: Logical: is std_X a filebacked big.matrix object? This is set internally by plmm().
trace: If set to TRUE, inform the user of progress by announcing the beginning of each step of the modeling process. Default is FALSE.
...: Not used

List with these components:

std_X: Standardized design matrix. If design matrix is filebacked, the descriptor for the filebacked data is returned using bigmemory::describe().
centered_y: Vector of centered outcomes
K: Similarity matrix
s: Vector of the non-zero eigenvalues of K
U: Matrix of eigenvectors of K associated with s (same as left singular values of X).
eta: The numeric value of the estimated eta parameter
penalty_factor A multiplicative factor for the penalty applied to each coefficient.
incpt_flag Logical: Does the model require fitting an intercept?
trace: If set to TRUE, inform the user of progress by announcing the beginning of each step of the modeling process