Author: Joshua Rosenberg

# Introduction to use of prcr for carrying a out two-step cluster analysis

#### Joshua Rosenberg

#### 09 February, 2020

# 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)

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.