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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# penfa
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[)`-brightgreen.svg)](https://github.com/egeminiani/penfa/commits/main)
[](https://egeminiani.github.io/penfa/)
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### Overview
An R package for estimating single- and multiple-group penalized factor models
via a trust-region algorithm with integrated automatic multiple tuning parameter
selection (Geminiani et al., 2021). Supported penalties include lasso, adaptive
lasso, scad, mcp, and ridge.
### Installation
You can install the released version of penfa from CRAN with:
``` r
install.packages("penfa")
```
And the development version from [GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("egeminiani/penfa")
```
### Example
This is a basic example showing how to fit a *PENalized Factor Analysis* model
with the alasso penalty and the automatic tuning procedure. A shrinkage penalty
is applied to the whole factor loading matrix.
Let's load the data (see `?ccdata` for details).
```{r data}
library(penfa)
data(ccdata)
```
<font size="4">**Step 1**</font> : specify the model syntax
```{r syntax}
syntax = 'help =~ h1 + h2 + h3 + h4 + h5 + h6 + h7 + 0*v1 + v2 + v3 + v4 + v5
voice =~ 0*h1 + h2 + h3 + h4 + h5 + h6 + h7 + v1 + v2 + v3 + v4 + v5'
```
<font size="4">**Step 2**</font>: fit the model
```{r fit}
alasso_fit <- penfa(model = syntax,
data = ccdata,
std.lv = TRUE,
pen.shrink = "alasso")
```
```{r show}
alasso_fit
```
<font size="4">**Step 3**</font>: inspect the results
```{r summary}
summary(alasso_fit)
```
### Vignettes and Tutorials
* See `vignette("automatic-tuning-selection")` for the estimation of a penalized
factor model with lasso and alasso penalties. The tuning parameter producing the
optimal amount of sparsity in the factor loading matrix is found through the
automatic tuning procedure.
* See `vignette("grid-search-tuning-selection")` for the estimation of a
penalized factor model with scad and mcp penalties. A grid search is conducted,
and the optimal tuning parameter is the one generating the penalized model with
the lowest GBIC (Generalized Bayesian Information Criterion).
* See ["multiple-group-analysis"](https://egeminiani.github.io/penfa/articles/articles/multiple-group-analysis.html) for the estimation of a multiple-group penalized factor model
with the alasso penalty. This model encourages sparsity in the loading matrices
and cross-group invariance of loadings and intercepts. The automatic multiple
tuning parameter procedure is employed for finding the optimal tuning parameter
vector.
* See ["plotting-penalty-matrix"](https://egeminiani.github.io/penfa/articles/articles/plotting-penalty-matrix.html) for details on how to produce interactive plots of the penalty matrices.
### Literature
* Geminiani, E., Marra, G., & Moustaki, I. (2021). "Single- and Multiple-Group
Penalized Factor Analysis: A Trust-Region Algorithm Approach with Integrated
Automatic Multiple Tuning Parameter Selection." Psychometrika, 86(1), 65-95. [https://doi.org/10.1007/s11336-021-09751-8](https://doi.org/10.1007/s11336-021-09751-8)
* Geminiani, E. (2020). "A Penalized Likelihood-Based Framework for Single and
Multiple-Group Factor Analysis Models." PhD thesis, University of Bologna.
[http://amsdottorato.unibo.it/9355/](http://amsdottorato.unibo.it/9355/).
### How to cite
```{r citation, echo=FALSE}
print(citation("penfa"), bibtex = TRUE)
```