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 built-in dataset 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)
mtcars_df <- as.data.frame(mtcars[, c("disp", "hp", "wt")])
two_profile_solution <- create_profiles(mtcars_df, 2, to_scale = T)
## Prepared data: Removed 0 incomplete cases
## Clustered data: Using a 2 cluster solution
## Calculated statistics: R-squared = 0.694
summary(two_profile_solution)
## 2 cluster solution (R-squared = 0.694)
## 
## Profile n and means:
## 
## # A tibble: 2 × 4
##               Cluster      disp        hp       wt
##                 <chr>     <dbl>     <dbl>    <dbl>
## 1 Cluster 1 (18 obs.) 0.5111562 0.5977198 0.764663
## 2 Cluster 2 (14 obs.) 1.3316418 1.2753130 1.172091
print(two_profile_solution)
## $clustered_processed_data
## 
## # A tibble: 2 × 4
##               Cluster      disp        hp       wt
##                 <chr>     <dbl>     <dbl>    <dbl>
## 1 Cluster 1 (18 obs.) 0.5111562 0.5977198 0.764663
## 2 Cluster 2 (14 obs.) 1.3316418 1.2753130 1.172091
## 
## $clustered_raw_data
## 
## # A tibble: 32 × 4
##         disp        hp        wt cluster
##        <dbl>     <dbl>     <dbl>   <int>
## 1  0.6034061 0.6705299 0.7678706       1
## 2  0.6034061 0.6705299 0.8426061       1
## 3  0.4072991 0.5669025 0.6799465       1
## 4  0.9729923 0.6705299 0.9422535       1
## 5  1.3576637 1.0667521 1.0081965       2
## 6  0.8485398 0.6400513 1.0140582       1
## 7  1.3576637 1.4934529 1.0462970       2
## 8  0.5532480 0.3779350 0.9349264       1
## 9  0.5309974 0.5790940 0.9232032       1
## 10 0.6320679 0.7497743 1.0081965       1
## # ... with 22 more rows
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 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$r_squared
## [1] 0.6937963
two_profile_solution$clustered_raw_data
## # A tibble: 32 × 4
##         disp        hp        wt cluster
##        <dbl>     <dbl>     <dbl>   <int>
## 1  0.6034061 0.6705299 0.7678706       1
## 2  0.6034061 0.6705299 0.8426061       1
## 3  0.4072991 0.5669025 0.6799465       1
## 4  0.9729923 0.6705299 0.9422535       1
## 5  1.3576637 1.0667521 1.0081965       2
## 6  0.8485398 0.6400513 1.0140582       1
## 7  1.3576637 1.4934529 1.0462970       2
## 8  0.5532480 0.3779350 0.9349264       1
## 9  0.5309974 0.5790940 0.9232032       1
## 10 0.6320679 0.7497743 1.0081965       1
## # ... with 22 more rows
two_profile_solution$clustered_processed_data
## # A tibble: 2 × 4
##               Cluster      disp        hp       wt
##                 <chr>     <dbl>     <dbl>    <dbl>
## 1 Cluster 1 (18 obs.) 0.5111562 0.5977198 0.764663
## 2 Cluster 2 (14 obs.) 1.3316418 1.2753130 1.172091

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.