## 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("jrosen48/tidyLPA")

## Example

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.

library(tidyLPA)
d <- pisaUSA15[1:100, ]

estimate_profiles(d,
n_profiles = 3)
#> Fit Equal variances and covariances fixed to 0 (model 1) model with 3 profiles.
#> LogLik is 283.991
#> BIC is 631.589
#> Entropy is 0.914
#> # A tibble: 94 x 5
#>    broad_interest enjoyment self_efficacy profile posterior_prob
#>             <dbl>     <dbl>         <dbl> <fct>            <dbl>
#>  1            3.8       4            1    1                1.000
#>  2            3         3            2.75 3                0.917
#>  3            1.8       2.8          3.38 3                0.997
#>  4            1.4       1            2.75 2                0.899
#>  5            1.8       2.2          2    3                0.997
#>  6            1.6       1.6          1.88 3                0.997
#>  7            3         3.8          2.25 1                0.927
#>  8            2.6       2.2          2    3                0.990
#>  9            1         2.8          2.62 3                0.998
#> 10            2.2       2            1.75 3                0.996
#> # ... with 84 more rows

The version of this function that uses MPlus is simple estimate_profiles_mplus() that is called in the same way (though some particular details can be changed with arguments specific to either estimate_profiles or to estimate_profiles_mplus()).

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).

We can plot the profiles with by piping (using the %>% operator, loaded from the dplyr package) the output to plot_profiles().

library(dplyr, warn.conflicts = FALSE)

estimate_profiles(d,
n_profiles = 3) %>%
plot_profiles(to_center = TRUE)

## Model specification

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 variance and 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 equal variances and covariances fixed to 0 model is specified by default.

These arguments allow for four models to be specified:

• Equal variances and covariances fixed to 0 (Model 1)
• Varying variances and covariances fixed to 0 (Model 2)
• Equal variances and equal covariances (Model 3)
• Varying variances and varying covariances (Model 6)

Two additional models (Models 4 and 5) can be fit using functions that provide an interface to the MPlus software. 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):

estimate_profiles(d,
variances = "varying",
covariances = "varying",
n_profiles = 3)

## Comparing a wide range of solutions

The function compare_solutions() estimates models with varying numbers of profiles and model specifications:

compare_solutions(d, broad_interest, enjoyment, self_efficacy)

The version that uses MPlus - compare_solutions_mplus() - is called in the same way; like for estimate_profiles() and estimate_profiles_mplus(), some particular details can be specified with arguments specific to compare_solutions() or compare_solutions_mplus().