---
title: "Reference Validation"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Reference Validation}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include=FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
library(ggpower)
```

ggpower validates core kernels against published reference examples. Direct
noncentral t, F, normal, and chi-square procedures use tight tolerances. Exact
enumeration is used where the grid is computationally feasible. Approximation-backed
procedures report method notes in the result object.

## Example 1: One-sample t, a priori

Target: $d = 0.625$, $\alpha = 0.05$ (one-tailed), power $= 0.95$.

**Expected:** $n = 30$, actual power $\approx 0.955144$, $df = 29$.

```{r ex1}
r1 <- power_compute("t_one_sample", "a_priori", d = 0.625, alpha = 0.05,
                      power = 0.95, tails = "one")
r1$outputs[c("total_sample_size", "actual_power", "df")]
```

## Example 2: Multiple regression omnibus, post hoc

$f^2 = 0.1111111$, $\alpha = 0.05$, $N = 95$, 5 predictors.

**Expected:** $\lambda \approx 10.556$, critical $F \approx 2.317$, $df_2 = 89$, power $\approx 0.674$.

```{r ex2}
r2 <- power_compute("f_mreg_omnibus", "post_hoc", f2 = 0.1111111,
                      alpha = 0.05, total_n = 95, predictors = 5)
r2$outputs[c("noncentrality_parameter", "critical_f", "denominator_df", "power")]
```

## Example 3: ANOVA special, post hoc

$f = 0.2450722$, $N = 108$, $df_1 = 4$, 36 groups.

**Expected:** $\lambda \approx 6.487$, $df_2 = 72$, power $\approx 0.476$.

```{r ex3}
r3 <- power_compute("f_anova_special", "post_hoc", f = 0.2450722,
                      alpha = 0.05, total_n = 108, df1 = 4, groups = 36)
r3$outputs[c("noncentrality_parameter", "denominator_df", "power")]
```

## Example 4: Two-sample t, unequal n, post hoc

$d = 0.5$, $n_1 = 4$, $n_2 = 8$, one-tailed $\alpha = 0.05$.

**Expected:** $\delta \approx 0.816$, $df = 10$, power $\approx 0.189$.

```{r ex4}
r4 <- power_compute("t_two_sample", "post_hoc", d = 0.5, n1 = 4, n2 = 8,
                      alpha = 0.05, tails = "one")
r4$outputs[c("noncentrality_parameter", "df", "power")]
```

## Recommended tolerances

| Kernel type | Tolerance |
|-------------|-----------|
| Direct distribution (t, F, z, $\chi^2$) | $10^{-5}$ to $10^{-4}$ |
| Integer a priori solvers | Sample size exact; actual power $\geq$ target |
| Approximation-backed | Document method; validate with sensitivity plots |

## Related

- [Support matrix](support-matrix.html)
- [Approximation catalog](https://yaoxiangli.github.io/ggpower/articles/approximation-catalog.html) (pkgdown only)
- [Formula reference](https://yaoxiangli.github.io/ggpower/articles/formula-reference.html) (pkgdown only)
