plmm_checks: plmm_checks

In plmmr: Penalized Linear Mixed Models for Correlated Data

plmm_checks

Description

plmm_checks

Usage

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

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 '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. Defaults to FALSE. Note: plmm() does not check to see if a matrix is diagonal. If you want to use a diagonal K matrix, you must set diag_K = TRUE.
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.
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.
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()

Value

A list of parameters to pass on to model fitting. The list includes the standardized design matrix, the outcome, and meta-data

plmmr documentation built on April 4, 2025, 12:19 a.m.