README.Rmd

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output: github_document
---

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# EFAtools

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The EFAtools package provides functions to perform exploratory factor analysis (EFA) procedures and compare their solutions. The goal is to provide state-of-the-art factor retention methods and a high degree of flexibility in the EFA procedures. This way, implementations from R psych and SPSS can be compared. Moreover, functions for Schmid-Leiman transformation, and computation of omegas are provided. To speed up the analyses, some of the iterative procedures like principal axis factoring (PAF) are implemented in C++.

## Installation

You can install the release version from [CRAN](https://cran.r-project.org/) with:
```{r eval=FALSE}
install.packages("EFAtools")
```

You can install the development version from [GitHub](https://github.com/) with:

```{r eval=FALSE}
install.packages("devtools")
devtools::install_github("mdsteiner/EFAtools")
```

To also build the vignette when installing the development version, use:

```{r eval=FALSE}
install.packages("devtools")
devtools::install_github("mdsteiner/EFAtools", build_vignettes = TRUE)
```

## Example

Here are a few examples on how to perform the analyses with the different types and how to compare the results using the `COMPARE` function. For more details, see the vignette by running `vignette("EFAtools", package = "EFAtools")`. The vignette provides a high-level introduction into the functionalities of the package.

```{r fig.height=3}
# load the package
library(EFAtools)

# Run all possible factor retention methods
N_FACTORS(test_models$baseline$cormat, N = 500, method = "ML")

# A type SPSS EFA to mimick the SPSS implementation with
# promax rotation
EFA_SPSS <- EFA(test_models$baseline$cormat, n_factors = 3, type = "SPSS",
                  rotation = "promax")

# look at solution
EFA_SPSS

# A type psych EFA to mimick the psych::fa() implementation with
# promax rotation
EFA_psych <- EFA(test_models$baseline$cormat, n_factors = 3, type = "psych",
                  rotation = "promax")

# compare the type psych and type SPSS implementations
COMPARE(EFA_SPSS$rot_loadings, EFA_psych$rot_loadings,
        x_labels = c("SPSS", "psych"))

# Average solution across many different EFAs with oblique rotations
EFA_AV <- EFA_AVERAGE(test_models$baseline$cormat, n_factors = 3, N = 500,
                      method = c("PAF", "ML", "ULS"), rotation = "oblique",
                      show_progress = FALSE)

# look at solution
EFA_AV

# Perform a Schmid-Leiman transformation
SL <- SL(EFA_psych)

# Based on a specific salience threshold for the loadings (here: .20):
factor_corres <- SL$sl[, c("F1", "F2", "F3")] >= .2

# Compute omegas from the Schmid-Leiman solution
OMEGA(SL, factor_corres = factor_corres)
```

## Citation

If you use this package in your research, please acknowledge it by citing: 

Steiner, M.D., & Grieder, S.G. (2020). EFAtools: An R package with fast and flexible implementations of exploratory factor analysis tools. *Journal of Open Source Software*, *5*(53), 2521. https://doi.org/10.21105/joss.02521

## Contribute or Report Bugs

If you want to contribute or report bugs, please open an issue on GitHub or email us at markus.d.steiner@gmail.com or silvia.grieder@gmail.com.