plmm_prep: PLMM prep: a function to run checks, SVD, and rotation prior to fitting a PLMM model This is an internal function for `cv_plmm`

Description

PLMM prep: a function to run checks, SVD, and rotation prior to fitting a PLMM model This is an internal function for cv_plmm

plmm_prep(
  std_X,
  std_X_n,
  std_X_p,
  n,
  p,
  centered_y,
  K = NULL,
  diag_K = NULL,
  eta_star = NULL,
  fbm_flag,
  trace = NULL,
  ...
)

List with these components:

centered_y: The vector of centered outcomes
std_X: standardized design matrix
K: a list with 2 elements. (1) s: vector with the eigenvalues of K, and (2) U: the eigenvectors of K (same as left singular values of X).
eta: the numeric value of the estimated eta parameter
trace: logical.

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)
n: The number of instances in the original design matrix X. This should not be altered by standardization.
p: The number of features in the original design matrix X, including constant features
centered_y: Continuous outcome vector, centered.
K: Similarity matrix used to rotate the data. This should either be a known matrix that reflects the covariance of y, or an estimate (Default is \(\frac{1}{p}(XX^T)\), where X is standardized). This can also be a list, with components d and u (as returned by choose_k)
diag_K: Logical: should K be a diagonal matrix? This would reflect observations that are unrelated, or that can be treated as unrelated. Passed from plmm().
eta_star: 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 an FBM type 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 yet