test_f {plsmmLasso}

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,
  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.

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]]