tune_plsmm: Tune Penalized PLSMM

In plsmmLasso: Variable Selection and Inference for Partial Semiparametric Linear Mixed-Effects Model

Tune Penalized PLSMM

Description

This function tunes a penalized partial linear semiparametric mixed-model (PLSMM) by performing a grid search over a set of hyperparameters to find the best model based on a given criterion.

Usage

tune_plsmm(
  x,
  y,
  series,
  t,
  name_group_var,
  bases,
  gamma_vec,
  lambda_vec,
  timexgroup,
  criterion,
  ...
)

Arguments

x	A matrix of predictors.
y	A continuous vector of response variable.
series	A variable representing different series or groups in the data modeled as a random intercept.
t	A numeric vector indicating the time points.
name_group_var	A character string specifying the name of the grouping variable.
bases	A matrix of bases functions.
gamma_vec	A vector of values for the regularization parameter for the coefficients of the nonlinear functions.
lambda_vec	A vector of values for the regularization parameter for the coefficients of the fixed effects.
timexgroup	Logical indicating whether to use a time-by-group interaction. If TRUE, each group in name_group_var will have its own estimate of the time effect.
criterion	A character string specifying the criterion to be optimized ('BIC', 'BICC', 'EBIC').
...	Additional arguments to be passed to the plsmm_lasso function.

Details

This function performs a grid search over the hyperparameters specified by lambda_vec and gamma_vec to find the best-fitted PLSMM based on the given criterion. It fits PLSMMs using the plsmm_lasso function for each combination of hyperparameters and retains only the models that have converged. The best model is selected based on the minimum value of the specified criterion.

Value

A PLSMM object representing the best-tuned model based on the specified criterion.

See Also

plsmm_lasso

Examples

set.seed(123)
data_sim <- simulate_group_inter(
 N = 50, n_mvnorm = 3, grouped = TRUE,
 timepoints = 3:5, nonpara_inter = TRUE,
 sample_from = seq(0, 52, 13),
 cos = FALSE, A_vec = c(1, 1.5)
)
sim = data_sim$sim
x = as.matrix(sim[,-1:-3])
y = sim$y
series = sim$series
t = sim$t
bases = create_bases(t)
lambdas <- c(0.0046, 0.0001)
gammas <- 0.00000001
tuned_plsmm <- tune_plsmm(x, y, series, t,
                      name_group_var = "group", bases$bases,
                      gamma_vec = gammas, lambda_vec = lambdas, timexgroup = TRUE,
                      criterion = "BIC"
)

plsmmLasso documentation built on June 22, 2024, 9:35 a.m.