plmm_checks
Usage
plmm_checks(
design,
K = NULL,
diag_K = NULL,
eta_star = NULL,
penalty = "lasso",
init = NULL,
gamma,
alpha = 1,
dfmax = NULL,
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.
- dfmax
Option to be added soon: Upper bound for the number of nonzero coefficients. Default is no upper bound. However, for large data sets, computational burden may be heavy for models with a large number of nonzero coefficients.
- 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
plmmobject in the current (assumed interactive) session. Defaults to TRUE.- ...
Additional arguments to
get_data()