plmm_checks: A function to perform checks on passed objects before model fitting.

Description

A function to perform checks on passed objects before model fitting.

Usage

plmm_checks(
  design,
  K = NULL,
  eta = NULL,
  penalty = "lasso",
  init = NULL,
  gamma,
  alpha = 1,
  dfmax = NULL,
  trace = FALSE,
  save_rds = NULL,
  return_fit = TRUE,
  ...
)

Value

A list which includes 16 items:

std_X: The standardized design matrix. If design matrix is filebacked, the descriptor for the filebacked data is returned using bigmemory::describe().
std_X_details: Metadata for std_X.
std_X_n: Number of rows in std_X.
std_X_p: Number of columns in std_X.
y: Original outcome vector.
y_name: Variable name of y.
centered_y: The centered outcome vector.
K: The relationship matrix (as passed by plmm(), may be NULL)
eta: Estimated proportion of the variance in the outcome attributable to population/correlation structure (as passed by plmm(), may be NULL)
fbm_flag: Logical, is std_X filebacked?
plink_flag: Logical, does std_X originate from PLINK files?
penalty: A character string indicating the penalty type.
gamma: Tuning parameter for the SCAD or MCP penalties.
init: Initialized values for beta coefficients.
dfmax: Maximum number of non-zero coefficients that may enter the model.
n: Number of rows in the original design matrix prior to standardization procedures.
p: Number of columns in the original design matrix prior to standardization procedures.

Arguments

design: The design object, as created by create_design()
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.
penalty: The penalty to be applied to the model. Either "MCP" (the default), "SCAD", or "lasso".
init: Initial values for coefficients. Default is 0 for all columns of X.
gamma: The tuning parameter of the MCP/SCAD penalty (see details). Default is 3 for MCP and 3.7 for SCAD.
alpha: Tuning parameter for the Mnet estimator which controls the relative contributions from the MCP/SCAD penalty and the ridge, or L2 penalty. alpha = 1 is equivalent to MCP/SCAD penalty, while alpha = 0 would be equivalent to ridge regression. However, alpha = 0 is not supported; alpha may be arbitrarily small, but not exactly 0.
dfmax: Maximum number of non-zero coefficients that may enter the model. Default is NULL (no maximum)
trace: If set to TRUE, inform the user of progress by announcing the beginning of each step of the modeling process. Default is FALSE.
save_rds: Optional: if a filepath and name is specified (e.g., save_rds = "~/dir/my_results.rds"), then the model results are saved to the provided location. Defaults to NULL, which does not save the result.
return_fit: Optional: a logical value indicating whether the fitted model should be returned as a plmm object in the current (assumed interactive) session. Defaults to TRUE.
...: Additional arguments to get_data()