---
title: "Introduction to use of prcr for carrying a out two-step cluster analysis"
author: "Joshua Rosenberg"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
  html_document: default
  pdf_document: default
vignette: |
  %\VignetteIndexEntry{Introduction}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---
  
# 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](https://www.oecd.org/pisa/data/2015database/Codebook_CMB.xlsx).
  
```{r, echo = F}
devtools::load_all(".")
```

```{r, eval = F}
library(prcr)
```

```{r, eval = T}
df <- pisaUSA15
m3 <- create_profiles_cluster(df, broad_interest, enjoyment, instrumental_mot, self_efficacy, n_profiles = 3)
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.