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.
You can install tidyLPA (version 0.1.2) from CRAN with:
You can also install the in-development version of tidyLPA from GitHub with:
Here is a brief example using the built-in
pisaUSA15 dataset 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.
d <- pisaUSA15[1:100, ] estimate_profiles(d, broad_interest, enjoyment, self_efficacy, n_profiles = 3, model = 2) #> Fit varying means, equal variances and covariances (Model 2) model with 3 profiles. #> LogLik is 279.692 #> BIC is 636.62 #> Entropy is 0.798 #> # A tibble: 94 x 5 #> broad_interest enjoyment self_efficacy profile posterior_prob #> <dbl> <dbl> <dbl> <fct> <dbl> #> 1 3.80 4.00 1.00 1 0.976 #> 2 3.00 3.00 2.75 2 0.847 #> 3 1.80 2.80 3.38 2 0.982 #> 4 1.40 1.00 2.75 3 0.963 #> 5 1.80 2.20 2.00 3 0.824 #> 6 1.60 1.60 1.88 3 0.960 #> 7 3.00 3.80 2.25 1 0.847 #> 8 2.60 2.20 2.00 3 0.704 #> 9 1.00 2.80 2.62 3 0.584 #> 10 2.20 2.00 1.75 3 0.861 #> # ... with 84 more rows
See the output is simply a data frame with the profile (and its posterior probability) and the variables used to create the profiles (this is the “tidy” part, in that the function takes and returns a data frame).
In addition to the number of profiles (specified with the
n_profiles argument), the model is important. The
model argument allows for four models to be specified:
Two additional models can be fit using functions that provide an interface to the MPlus software. More information on the models can be found in the vignette.
We can plot the profiles with by piping (using the
%>% operator, loaded from the
dplyr package) the output to
library(dplyr) #> #> Attaching package: 'dplyr' #> The following objects are masked from 'package:stats': #> #> filter, lag #> The following objects are masked from 'package:base': #> #> intersect, setdiff, setequal, union estimate_profiles(d, broad_interest, enjoyment, self_efficacy, n_profiles = 3, model = 2) %>% plot_profiles(to_center = TRUE) #> Fit varying means, equal variances and covariances (Model 2) model with 3 profiles. #> LogLik is 279.692 #> BIC is 636.62 #> Entropy is 0.798
To learn more:
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
As tidyLPA is at an early stage of its development, issues should be expected. If you have any questions or feedback, please do not hesitate to get in touch:
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.