generate_gfm_data

Generate general factor model with smooth latent transformation

Implements general unilateral loading estimator for two-layer latent factor models with smooth, element-wise factor transformations. We provide data simulation, loading estimation,finite-sample error bounds, and diagnostic tools for zero-mean and sub-Gaussian assumptions. A unified interface is given for evaluating estimation accuracy and cosine similarity. The philosophy of the package is described in Guo G. (2026) <doi:10.1016/j.apm.2025.116280>.

Guangbao Guo

GulFM

General Unilateral Load Estimator for Two-Layer Latent Factor
Models

generate_gfm_data function

<dl><dt>n</dt>
<dd>Integer: sample size.</dd>
<dt>p</dt>
<dd>Integer: number of observed variables.</dd>
<dt>m</dt>
<dd>Integer: number of latent factors (both layers).</dd>
<dt>g_fun</dt>
<dd>Function: smooth, element-wise transformation applied to latent
factors. Must be vectorised, e.g. `sin`, `tanh`, `scale`.</dd>
<dt>seed</dt>
<dd>1.</dd>
<dt>sigma_V</dt>
<dd>Numeric: standard deviation of the idiosyncratic noise
(default 0.1 =&gt; Var = 0.01).</dd></dl>

Arguments

Generate general factor model with smooth latent transformation — generate_gfm_data

<dl>

<dt>n</dt>
<dd>Integer: sample size.</dd>


<dt>p</dt>
<dd>Integer: number of observed variables.</dd>


<dt>m</dt>
<dd>Integer: number of latent factors (both layers).</dd>


<dt>g_fun</dt>
<dd>Function: smooth, element-wise transformation applied to latent
factors. Must be vectorised, e.g. `sin`, `tanh`, `scale`.</dd>


<dt>seed</dt>
<dd>1.</dd>


<dt>sigma_V</dt>
<dd>Numeric: standard deviation of the idiosyncratic noise
(default 0.1 =&gt; Var = 0.01).</dd>

</dl>

generate_gfm_data: Generate general factor model with smooth latent transformation

Description

Usage

Value

Arguments

Examples