tune_plsmm {plsmmLasso}

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)