Background

prcr is an R package for person-centered analysis. Person-centered analyses focus on clusters, or profiles, of observations, and their change over time or differences across factors. See Bergman and El-Khouri (1999) for a description of the analytic approach. See Corpus and Wormington (2014) for an example of person-centered analysis in psychology and education.

Example using mtcars

In this example using the built-in to R mtcars data for fuel consumption and other information for 32 automobiles, the variables disp (for engine displacement, in cu. in.), qsec (for the 1/4 mile time, in seconds), and wt for weight (in 1000 lbs.) are clustered with a 2 cluster solution specified. Because the variables are in very different units, the to_scale argument is set to TRUE.

library(prcr)
df <- mtcars
two_profile_solution <- create_profiles(df, disp, hp, wt, n_profiles = 2, to_scale = T)
summary(two_profile_solution)
print(two_profile_solution)
plot(two_profile_solution)

The output has the class prcr and has slots for additional information that can be extracted from it, such as the original data with the clustering assignment added, the r-squared (for comparing the relative fit of different cluster solutions) raw clustered data (i.e., for conducting statistical tests to determine whether the cluster centroids are different from one another and for use in additional analyses) and the processed data (i.e., for creating different plots of the cluster centroids).

two_profile_solution$.data
two_profile_solution$r_squared
two_profile_solution$clustered_raw_data
two_profile_solution$clustered_processed_data

Functions for easily comparing the r-squared value for a range of cluster solutions, and for carrying out cross-validation of the clustering solution, will be added in future updates to the package.