minorbsem

minorbsem: An R package for structural equation models that account for the influence of minor factors - Published in JOSS (2023)

https://github.com/jamesuanhoro/minorbsem

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An R package for Bayesian structural equation models that account for the influence of minor factors

README.Rmd

---
output: github_document
bibliography: inst/REFERENCES.bib
---

# minorbsem

```{r include=FALSE}
# devtools::load_all()
```



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#### Table of Contents

- [Package overview](#package-overview)
  - [Goals](#goals)
  - [Permitted models and supported data types](#permitted-models-and-supported-data-types)
- [Installation](#installation)
- [A reasonably complete demonstration](#a-reasonably-complete-demonstration)
- [Additional examples](#additional-examples)
  - [Different methods to capture the influence of minor factors](#different-methods-to-capture-the-influence-of-minor-factors)
  - [Relax simple structure](#relax-simple-structure)
- [Contributions are encouraged](#contributions-are-encouraged)
- [Citations](#citations)

## Package overview

Structural equation models (SEMs) rarely reject the null hypothesis that there is 
no model misspecification. One explanation for this problem is that covariance 
structures are influenced by major factors which
we can hypothesize about and minor factors which we cannot predict a-priori,
e.g. @maccallum_representing_1991.

### Goals

The goal of `minorbsem` is to facilitate fitting Bayesian SEMs that estimate the
influence of minor factors on the covariance matrix, following the approach in
@uanhoro_modeling_2023. Briefly, the method estimates all residual covariances with 
priors that shrink them towards zero, and the model returns the magnitude of the
influence of minor factors.

The package also allows you set priors on all substantive model parameters
directly. Importantly, prior distributions assume latent variables have a
total variance of 1, even in latent regression models.

### Permitted models and supported data types

The package is able to fit a variety of model configurations:

- CFA, allowing automatically estimated penalized cross-loadings
- Path models with latent and observed variables
  - Any observed variables in a structural model must be represented with a
  single-indicator latent variable with the error variance of the observed
  variable constrained to 0

The package is also able to **analyze correlation structures** using methods in
@archakov_new_2021. This includes polychoric correlation matrices as long as
an asymptotic variance matrix is provided. The relevant paper is under review
at Structural Equation Modeling.

The package does not support fitting multi-group or multilevel models.
See the [bayesianmasem](https://github.com/jamesuanhoro/bayesianmasem) package
for multi-group factor analysis via meta-analysis methods.

## Installation

`minorbsem` is hosted on GitHub, so we need the `remotes` package to
install it. We also need to install the `cmdstanr` package and CmdStan
in order to use Stan.

Instructions:

``` r
install.packages("remotes")  # install remotes

# next install cmdstanr and CmdStan:
install.packages(
  "cmdstanr",
  repos = c("https://mc-stan.org/r-packages/", getOption("repos"))
)
cmdstanr::check_cmdstan_toolchain(fix = TRUE)
cmdstanr::install_cmdstan()

# Then finally minorbsem:
remotes::install_github("jamesuanhoro/minorbsem")
```

## A reasonably complete demonstration

``` r
library(minorbsem)
# Basic Holzinger-Swineford model
syntax_1 <- "
F1 =~ x1 + x2 + x3
F2 =~ x4 + x5 + x6
F3 =~ x7 + x8 + x9"
# Expect a summary table output
fit_1 <- minorbsem(syntax_1, HS)

# Save output table to html file, see: ?pretty_print_summary for more options
pretty_print_summary(fit_1, save_html = "baseline_model.html")

# Histogram of parameters, see: ?parameter_hist for arguments
parameter_hist(fit_1)

# Traceplot of parameters, see: ?parameter_trace for arguments
parameter_trace(fit_1)

# Examine all standardized residual covariances
plot_residuals(fit_1)
plot_residuals(fit_1, type = "range")
```

## Additional examples

### Different methods to capture the influence of minor factors

Default method above is `method = "normal"` assuming standardized residual
covariances are on average 0 and vary from 0 in continuous fashion.

``` r
# Fit same model as above but use global-local prior to estimate
# minor factor influences
fit_gdp <- minorbsem(syntax_1, HS, method = "GDP")
plot_residuals(fit_gdp)
parameter_hist(fit_gdp)
parameter_trace(fit_gdp)

# Ignoring minor factor influences
fit_none <- minorbsem(syntax_1, HS, method = "none")
parameter_hist(fit_none)
parameter_trace(fit_none)

# Error!!!: Plotting residuals will give an error message
# since minor factor influences are assumed null
plot_residuals(fit_none)
```

### Relax simple structure

```r
fit_complex <- minorbsem(syntax_1, HS, simple_struc = FALSE)
```

There are other methods, see details section in `?minorbsem`.

## Contributions are encouraged

All users of R (or SEM) are invited to submit functions or ideas for functions.

Feel free to:

- [open an issue](https://github.com/jamesuanhoro/minorbsem/issues/) to report a
bug or to discuss recommendations;
- submit pull requests to recommend modifications or suggest improvements.

You can also email the package maintainer, James Uanhoro (James dot Uanhoro at unt dot edu).
Thank you for helping improve minorbsem :).

## Citations

Owner

JOSS Publication

minorbsem: An R package for structural equation models that account for the influence of minor factors

Published

June 21, 2023

DOI

10.21105/joss.05292

Volume 8, Issue 86, Page 5292

Authors

James Ohisei Uanhoro
Department of Educational Psychology, University of North Texas, USA

Editor

Samuel Forbes

Tags

Bayesian-statistics latent-variable-models structural-equation-modeling psychometrics meta-analytic-SEM

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