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.
You can install tidyLPA from CRAN with:
You can also install the development version of tidyLPA from GitHub with:
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
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 3 629.24 665.71 0.80 0.83 0.95 0.03 0.66 0.01
A 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.
We can use Mplus simply by changing the package argument for
pisaUSA15[1:100, ] %>% select(broad_interest, enjoyment, self_efficacy) %>% single_imputation() %>% estimate_profiles(3, package = "MplusAutomation")
To learn more, we highly recommend the following:
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
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:
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.
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.
This material is based upon work supported by the National Science Foundation under Grant No.: DRL#1661064. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not reflect the views of the National Science Foundation.
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