---
title: "Getting Started with piiR"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Getting Started with piiR}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

## What is the Predictive Information Index (PII)?

The Predictive Information Index (PII) quantifies how much outcome-relevant information is retained when reducing a set of predictors (e.g., items) to a composite score.

One version of PII, the **variance-based form**, is defined as:

```math
\text{PII}_{v} = 1 - \frac{\text{Var}(\hat{Y}_{\text{Full}} - \hat{Y}_{\text{Score}})}{\text{Var}(\hat{Y}_{\text{Full}})}

```

Where:
- \( \hat{Y}_{\text{Full}} \): predictions from a full model (e.g., all items or predictors)
- \( \hat{Y}_{\text{Score}} \): predictions from a reduced score (e.g., mean or sum)

A PII of 1 means no predictive information was lost. A PII near 0 means the score loses most predictive information.

## Example: Using `pii()`

```{r}
library(piiR)

# Simulate an outcome and two prediction vectors
set.seed(123)
y     <- rnorm(100)                        # observed outcome
full  <- y + rnorm(100, sd = 0.3)          # full-model predictions
score <- y + rnorm(100, sd = 0.5)          # score-based predictions

# Compute the three PII variants
pii(y, score, full, type = "r2")  # variance explained
pii(y, score, full, type = "rm")  # RMSE ratio
pii(y, score, full, type = "v")   # variance ratio
```

