Example

In this example using the built-in to prcr dataset pisaUSA15. Specifically, we use composite variables for broad interest, enjoyment, instrumental motivation, and self-efficacy. More information on these and other items can be found at this link.

## Loading prcr

library(prcr)

df <- pisaUSA15
m3 <- create_profiles_cluster(df, broad_interest, enjoyment, instrumental_mot, self_efficacy, n_profiles = 3)

## Prepared data: Removed 354 incomplete cases

## Hierarchical clustering carried out on: 5358 cases

## K-means algorithm converged: 5 iterations

## Clustered data: Using a 3 cluster solution

## Calculated statistics: R-squared = 0.424

plot_profiles(m3, to_center = TRUE)

## Warning: attributes are not identical across measure variables;
## they will be dropped

Other functions include those for carrying out comparing r-squared values and perfomring cross-validation. These are documented in the CRAN release and their versions in the in-development version will be documented prior to the CRAN release.