plmm_fit: PLMM fit: a function that fits a PLMM using the values...

In plmmr: Penalized Linear Mixed Models for Correlated Data

PLMM fit: a function that fits a PLMM using the values returned by plmm_prep()

Description

PLMM fit: a function that fits a PLMM using the values returned by plmm_prep()

Usage

plmm_fit(
  prep,
  y,
  std_X_details,
  eta_star,
  penalty_factor,
  fbm_flag,
  penalty,
  gamma = 3,
  alpha = 1,
  lambda_min,
  nlambda = 100,
  lambda,
  eps = 1e-04,
  max_iter = 10000,
  init = NULL,
  warn = TRUE,
  ...
)

Arguments

prep	A list as returned from plmm_prep
y	The original (not centered) outcome vector. Need this for intercept estimate
std_X_details	A list with components 'center' (values used to center X), 'scale' (values used to scale X), and 'ns' (indices for nonsignular columns of X)
eta_star	The ratio of variances (passed from plmm())
penalty_factor	A multiplicative factor for the penalty applied to each coefficient. If supplied, penalty_factor must be a numeric vector of length equal to the number of columns of X. The purpose of penalty_factor is to apply differential penalization if some coefficients are thought to be more likely than others to be in the model. In particular, penalty_factor can be 0, in which case the coefficient is always in the model without shrinkage.
fbm_flag	Logical: is std_X an FBM object? Passed from plmm().
penalty	The penalty to be applied to the model. Either "MCP" (the default), "SCAD", or "lasso".
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.
lambda_min	The smallest value for lambda, as a fraction of lambda.max. Default is .001 if the number of observations is larger than the number of covariates and .05 otherwise.
nlambda	Length of the sequence of lambda. Default is 100.
lambda	A user-specified sequence of lambda values. By default, a sequence of values of length nlambda is computed, equally spaced on the log scale.
eps	Convergence threshold. The algorithm iterates until the RMSD for the change in linear predictors for each coefficient is less than eps. Default is 1e-4.
max_iter	Maximum number of iterations (total across entire path). Default is 10000.
init	Initial values for coefficients. Default is 0 for all columns of X.
warn	Return warning messages for failures to converge and model saturation? Default is TRUE.
...	Additional arguments that can be passed to biglasso::biglasso_simple_path()

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