## 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.

## 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")

Please note that some functions documented on the References page may only be available in the development version of tidyLPA. Also note that if an error occurs, you may try the development version to see if the issue is addressed.

## Example

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.

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

estimate_profiles(d,
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:

• Varying means, equal variances, and covariances fixed to 0 (model 1)
• Varying means, equal variances, and equal covariances (model 2)
• Varying means, varying variances, and covariances fixed to 0 (model 3)
• Varying means, varying variances, and varying covariances (model 6)

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 plot_profiles().

estimate_profiles(d,
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