GulPC_TFM

This function performs General Unilateral Loading Principal Component (GulPC) analysis on a given data set. It calculates the estimated values for the first layer and second layer loadings, specific variances, and the mean squared errors.

The Truncated Factor Model is a statistical model designed to handle specific data structures in data analysis. This R package focuses on the Sparse Online Principal Component Estimation method, which is used to calculate data such as the loading matrix and specific variance matrix for truncated data, thereby better explaining the relationship between common factors and original variables. Additionally, the R package also provides other equations for comparison with the Sparse Online Principal Component Estimation method.The philosophy of the package is described in thesis. (2023) <doi:10.1007/s00180-022-01270-z>.

Guangbao Guo

Sparse Online Principal Component for Truncated Factor Model

Beibei Wu

GulPC_TFM function

<dl><dt>data</dt>
<dd>A matrix of input data.</dd>
<dt>m</dt>
<dd>The number of principal components.</dd>
<dt>A</dt>
<dd>The true factor loadings matrix.</dd>
<dt>D</dt>
<dd>The true uniquenesses matrix.</dd></dl>

Arguments

Apply the GulPC method to the Truncated factor model — GulPC_TFM

<dl>

<dt>data</dt>
<dd>A matrix of input data.</dd>


<dt>m</dt>
<dd>The number of principal components.</dd>


<dt>A</dt>
<dd>The true factor loadings matrix.</dd>


<dt>D</dt>
<dd>The true uniquenesses matrix.</dd>

</dl>

GulPC_TFM: Apply the GulPC method to the Truncated factor model

Description

Usage

Value

Arguments

Examples