tidyLPA has undergone some major changes. You can read about them here.
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
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") #> tidyLPA analysis using mplus: #> #> Model Classes AIC BIC Entropy prob_min prob_max n_min n_max #> 1 3 629.462 665.934 0.812 0.858 0.951 0.030 0.630 #> BLRT_p #> 0.000
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
get_fit, etc. This is the “tidy” part, in that the function can be embedded in a tidy analysis pipeline.
If you have Mplus installed, you can call the version of this function that uses MPlus in the same way, by adding the argument
package = "MplusAutomation.
In addition to the number of profiles (specified with the
n_profiles argument), the model can be specified in terms of whether and how the variable variances and covariances are estimated.
The models are specified by passing arguments to the
covariance arguments. The possible values for these arguments are:
variances: “equal” and “zero”
covariances: “varying”, “equal”, and “zero”
If no values are specified for these, then the variances are constrained to be equal across classes, and covariances are fixed to 0 (conditional independence of the indicators).
These arguments allow for four models to be specified:
Two additional models (Models 4 and 5) can be fit using MPlus. More information on the models can be found in the vignette.
Here is an example of specifying a model with varying variances and covariances (Model 6; not run here):
compare_solutions() compares the fit of several estimated models, with varying numbers of profiles and model specifications:
A few helper functions are available to make it easier to work with the output of an analysis.
get_data() returns the data:
m <- pisaUSA15[1:100, ] %>% select(broad_interest, enjoyment, self_efficacy) %>% single_imputation() %>% estimate_profiles(3:4) get_data(m) #> # A tibble: 700 x 9 #> model_number classes_number broad_interest enjoyment self_efficacy Class #> <dbl> <int> <dbl> <dbl> <dbl> <dbl> #> 1 1 3 3.8 4 1 1 #> 2 1 3 3 3 2.75 2 #> 3 1 3 1.8 2.8 3.38 2 #> 4 1 3 1.4 1 2.75 2 #> 5 1 3 1.8 2.2 2 3 #> 6 1 3 1.6 1.6 1.88 3 #> 7 1 3 3 3.8 2.25 3 #> 8 1 3 2.6 2.2 2 3 #> 9 1 3 1 2.8 2.62 2 #> 10 1 3 2.2 2 1.75 3 #> # … with 690 more rows, and 3 more variables: Class_prob <int>, #> # Probability <dbl>, id <int>
We note that
get_data() returns data in wide format when applied to an object of class tidyProfile (one element of a tidyLPA object), or when applied to a tidyLPA object of length one.
get_data() returns long format when applied to a tidyLPA object containing multiple tidyProfile analyses (because then the wide format does not make sense).
To transform data in the wide format into the long format, the
gather() function from the tidyr package can be used, e.g.:
get_data(m) %>% tidyr::gather(Class_prob, Probability, contains("CPROB")) #> # A tibble: 700 x 9 #> model_number classes_number broad_interest enjoyment self_efficacy Class #> <dbl> <int> <dbl> <dbl> <dbl> <dbl> #> 1 1 3 3.8 4 1 1 #> 2 1 3 3 3 2.75 2 #> 3 1 3 1.8 2.8 3.38 2 #> 4 1 3 1.4 1 2.75 2 #> 5 1 3 1.8 2.2 2 3 #> 6 1 3 1.6 1.6 1.88 3 #> 7 1 3 3 3.8 2.25 3 #> 8 1 3 2.6 2.2 2 3 #> 9 1 3 1 2.8 2.62 2 #> 10 1 3 2.2 2 1.75 3 #> # … with 690 more rows, and 3 more variables: Class_prob <int>, #> # Probability <dbl>, id <int>
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
Rosenberg, J. M., Beymer, P. N., Anderson, D. 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
You can also cite the most latest version with the following citation:
Rosenberg, J. M., van Lissa, C. J., Beymer, P. N., Anderson, D. J., Schell, M. J. & Schmidt, J. A. (2019). tidyLPA: Easily carry out Latent Profile Analysis (LPA) using open-source or commercial software [R package]. https://data-edu.github.io/tidyLPA/
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