prcr

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

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)
## Prepared data: Removed 0 incomplete cases
## Hierarchical clustering carried out on: 32 cases
## K-means algorithm converged: 1 iteration
## Clustered data: Using a 2 cluster solution
## Calculated statistics: R-squared = 0.654
summary(two_profile_solution)
## 2 cluster solution (R-squared = 0.654)
##
## Profile n and means:
##
## # A tibble: 2 x 4
##               Cluster     disp       hp       wt
##                 <chr>    <dbl>    <dbl>    <dbl>
## 1 Profile 1 (18 obs.) 1.093596 1.430155 2.666499
## 2 Profile 2 (14 obs.) 2.848989 3.051423 4.087264
print(two_profile_solution)
## $clustered_processed_data ## ## # A tibble: 2 x 4 ## Cluster disp hp wt ## <chr> <dbl> <dbl> <dbl> ## 1 Profile 1 (18 obs.) 1.093596 1.430155 2.666499 ## 2 Profile 2 (14 obs.) 2.848989 3.051423 4.087264 plot(two_profile_solution) The output has the class prcr and has slots for additional information that can be extracted from it, such as the original data with the clustering assignment added, the r-squared (for comparing the relative fit of different cluster solutions) raw clustered data (i.e., for conducting statistical tests to determine whether the cluster centroids are different from one another and for use in additional analyses) and the processed data (i.e., for creating different plots of the cluster centroids). two_profile_solution$.data
## # A tibble: 32 x 12
##      mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
##    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
##  1  21.0     6 160.0   110  3.90 2.620 16.46     0     1     4     4
##  2  21.0     6 160.0   110  3.90 2.875 17.02     0     1     4     4
##  3  22.8     4 108.0    93  3.85 2.320 18.61     1     1     4     1
##  4  21.4     6 258.0   110  3.08 3.215 19.44     1     0     3     1
##  5  18.7     8 360.0   175  3.15 3.440 17.02     0     0     3     2
##  6  18.1     6 225.0   105  2.76 3.460 20.22     1     0     3     1
##  7  14.3     8 360.0   245  3.21 3.570 15.84     0     0     3     4
##  8  24.4     4 146.7    62  3.69 3.190 20.00     1     0     4     2
##  9  22.8     4 140.8    95  3.92 3.150 22.90     1     0     4     2
## 10  19.2     6 167.6   123  3.92 3.440 18.30     1     0     4     4
## # ... with 22 more rows, and 1 more variables: cluster <int>
two_profile_solution$r_squared ## [1] 0.6544283 two_profile_solution$clustered_raw_data
## # A tibble: 32 x 4
##         disp        hp       wt cluster
##        <dbl>     <dbl>    <dbl>   <int>
##  1 1.2909608 1.6043669 2.677684       1
##  2 1.2909608 1.6043669 2.938298       1
##  3 0.8713986 1.3564193 2.371079       1
##  4 2.0816744 1.6043669 3.285784       1
##  5 2.9046619 2.5524020 3.515738       2
##  6 1.8154137 1.5314412 3.536178       1
##  7 2.9046619 3.5733627 3.648600       2
##  8 1.1836497 0.9042796 3.260234       1
##  9 1.1360455 1.3855896 3.219353       1
## 10 1.3522815 1.7939739 3.515738       1
## # ... with 22 more rows
two_profile_solution\$clustered_processed_data
## # A tibble: 2 x 4
##               Cluster     disp       hp       wt
##                 <chr>    <dbl>    <dbl>    <dbl>
## 1 Profile 1 (18 obs.) 1.093596 1.430155 2.666499
## 2 Profile 2 (14 obs.) 2.848989 3.051423 4.087264

Comparison of R-squared values can be carried out as follows:

r_squared_output <- plot_r_squared(df,
disp, hp, wt,
to_scale = T,
r_squared_table = TRUE,
lower_bound = 2, upper_bound = 4)
r_squared_output
##   cluster r_squared_value
## 1       2           0.654
## 2       3           0.750
## 3       4           0.833

Cross-validation is now able to be carried out, in this example for the two-profile solution, although n_profiles can also be set to the character string"iterate" in order to explore cross-validation output for a range of profile solutions:

cross_validation_output <- cross_validate(df,
disp, hp, wt,
to_scale = TRUE,
n_profiles = 2,
distance_metric = "squared_euclidean",
k = 30)
cross_validation_output
## # A tibble: 30 x 3
##    k_iteration kappa percentage_agree
##          <int> <dbl>            <dbl>
##  1           1 -0.05                0
##  2           2 -0.04                0
##  3           3 -0.03                0
##  4           4 -0.04                0
##  5           5 -0.04                0
##  6           6 -0.04                0
##  7           7 -0.04                0
##  8           8 -0.04                0
##  9           9    NA               NA
## 10          10 -0.04                0
## # ... with 20 more rows

Due to the very small sample size, these results are just for illustrative purposes.