Background
Latent Profile Analysis (LPA) is a statistical modeling approach for estimating distinct profiles, or groups, of variables. In the social sciences and in educational research, these profiles could represent, for example, how different youth experience dimensions of being engaged (i.e., cognitively, behaviorally, and affectively) at the same time.
tidyLPA provides the functionality to carry out LPA in R. In particular, tidyLPA provides functionality to specify different models that determine whether and how different parameters (i.e., means, variances, and covariances) are estimated and to specify (and compare solutions for) the number of profiles to estimate. The package is designed and documented to be easy to use, especially for beginners to LPA, but with fine-grained options available for estimating models and evaluating specific output as part of more complex analyses.
Installation
You can install tidyLPA from CRAN with:
install.packages("tidyLPA")You can also install the development version of tidyLPA from GitHub with:
install.packages("devtools")
devtools::install_github("data-edu/tidyLPA")Examples
Mclust
Here is a brief example using the built-in pisaUSA15 data set and variables for broad interest, enjoyment, and self-efficacy. Note that we first type the name of the data frame, followed by the unquoted names of the variables used to create the profiles. We also specify the number of profiles and the model. See ?estimate_profiles for more details.
In these examples, we pass the results of one function to the next by piping (using the %>% operator, loaded from the dplyr package). We pass the data to a function that selects relevant variables, and then to estimate_profiles:
pisaUSA15[1:100, ] %>%
select(broad_interest, enjoyment, self_efficacy) %>%
single_imputation() %>%
estimate_profiles(3)
#> tidyLPA analysis using mclust:
#>
#> Model Classes AIC BIC Entropy prob_min prob_max n_min n_max BLRT_p
#> 1 3 629.72 666.20 0.80 0.84 0.95 0.04 0.67 0.01A simple summary of the analysis is printed to the console (and its posterior probability).
The resulting object can be further passed down a pipeline to other functions, such as:
This is the “tidy” part, in that the function can be embedded in a tidy analysis pipeline.
Mplus
We can use MPlus simply by changing the package argument for estimate_profiles() to "MplusAutomation" (please note that MPlus must be installed on your computer for this functionality to work):
pisaUSA15[1:100, ] %>%
select(broad_interest, enjoyment, self_efficacy) %>%
single_imputation() %>%
estimate_profiles(3, package = "MplusAutomation")To learn more, we highly recommend the following:
- Browse the tidyLPA website (especially check out the Reference page to see more about other functions)
- Read the Introduction to tidyLPA vignette, which has much more information on the models that can be specified with tidyLPA and on additional functionality
Citing tidyLPA
Rosenberg, J. M., Beymer, P. N., Anderson, D. J., Van Lissa, C. J., & Schmidt, J. A. (2018). tidyLPA: An R Package to Easily Carry Out Latent Profile Analysis (LPA) Using Open-Source or Commercial Software. Journal of Open Source Software, 3(30), 978, https://doi.org/10.21105/joss.00978
Contributing and Contact Information
One of the easiest but also most important ways to contribute is to post a question or to provide feedback. Both positive and negative feedback is welcome and helpful. You can get in touch by:
Sending a message via tidylpa@googlegroups.com or view the the tidyLPA group page
Filing an issue on GitHub here
Contributions are also welcome via by making pull requests (PR), e.g. through this page on GitHub.
It may be easier if you first file an issue outlining what you will do in the PR. You can also reach out via the methods described above.
Contributor Code of Conduct
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
