test_f: Bootstrap joint confidence bands and L2-norm based test on...

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

Bootstrap joint confidence bands and L2-norm based test on nonlinear functions

Description

This function conducts a test of overall equality of two nonlinear functions and generates confidence bands for the estimated difference of the nonlinear functions using a bootstrap method.

Usage

test_f(
  x,
  y,
  series,
  t,
  name_group_var,
  plsmm_output,
  n_boot = 1000,
  predicted = FALSE,
  show_obs = FALSE,
  verbose = TRUE
)

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.
plsmm_output	Output object obtained from the plsmm_lasso function.
n_boot	Numeric specifying the number of bootstrap samples (default is 1000).
predicted	Logical indicating whether to plot predicted values. If FALSE only the observed time points are used.
show_obs	Logical. If TRUE the observed time points are used for the position scale of the x-axis.
verbose	Logical indicating whether to display bootstrap progress. Default is TRUE.

Details

The function generate bootstrap samples and estimate the nonlinear functions for each n_boot sample. These bootstrap estimates are then used to compute the L2-norm test of equality and the joint confidence bands.

Value

A plot showing the estimated difference and confidence bands of the nonlinear functions.

A list containing:

overall_test_results	Results from the L2-norm test of equality.
CI_f	Confidence intervals values for the difference of the estimated functions used for plotting.

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)
lambda <- 0.0046
gamma <- 0.00000001
plsmm_output <- plsmm_lasso(x, y, series, t,
  name_group_var = "group", bases$bases,
  gamma = gamma, lambda = lambda, timexgroup = TRUE,
  criterion = "BIC"
)
test_f_results <- test_f(x, y, series, t,
 name_group_var = "group", plsmm_output,
 n_boot = 10
)
test_f_results[[1]]
test_f_results[[2]]

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