create_bases

The <code>create_bases</code> function generates a matrix of bases functions for modeling time.
The bases function matrix include Fourier basis functions and polynomial basis functions.

Implements a partial linear semiparametric mixed-effects model (PLSMM) featuring a random intercept and applies a lasso penalty to both the fixed effects and the coefficients associated with the nonlinear function.
The model also accommodates interactions between the nonlinear function and a grouping variable, allowing for the capture of group-specific nonlinearities. Nonlinear functions are modeled using a set of bases functions. Estimation is conducted using a penalized Expectation-Maximization algorithm, and the package offers flexibility in choosing between various information criteria for model selection.
Post-selection inference is carried out using a debiasing method, while inference on the nonlinear functions employs a bootstrap approach.

Sami Leon

plsmmLasso

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

Tong Tong Wu

create_bases function

<dl><dt>t</dt>
<dd>A vector representing the timepoints for which basis functions are generated.</dd>
<dt>keep</dt>
<dd>(Optional) A vector specifying the indices of the basis functions to retain.
If not provided, all generated basis functions are retained.</dd></dl>

Arguments

Generate bases function matrix — create_bases

<dl>

<dt>t</dt>
<dd>A vector representing the timepoints for which basis functions are generated.</dd>


<dt>keep</dt>
<dd>(Optional) A vector specifying the indices of the basis functions to retain.
If not provided, all generated basis functions are retained.</dd>

</dl>

create_bases: Generate bases function matrix

Description

Usage

Value

Arguments

Details

See Also

Examples