
---
title: "Automated Statistical Test"
author: "Wouter Zeevat"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Automated Statistical Test}
  %\VignetteEngine{knitr::rmarkdown}
  \usepackage{graphicx}
---

# Introduction

The `automatedtests` package automatically selects and runs the most appropriate statistical test for your data, no manual decision-making needed.
The function works with both individual vectors or a data frame and provides the results in an easy-to-understand format, which includes the test used and all the relevant statistics.

### Supported Tests
| Number | Test                                     |
| ------ | ---------------------------------------- |
| 1      | One-proportion test                      |
| 2      | Chi-square goodness-of-fit test          |
| 3      | One-sample Student's t-test              |
| 4      | One-sample Wilcoxon test                 |
| 5      | Multiple linear regression               |
| 6      | Binary logistic regression               |
| 7      | Multinomial logistic regression          |
| 8      | Pearson correlation                      |
| 9      | Spearman's rank correlation              |
| 10     | Cochran's Q test                         |
| 11     | McNemar's test                           |
| 12     | Fisher's exact test                      |
| 13     | Chi-square test of independence          |
| 14     | Student's t-test for independent samples |
| 15     | Welch's t-test for independent samples   |
| 16     | Mann-Whitney U test                      |
| 17     | Student's t-test for paired samples      |
| 18     | Wilcoxon signed-rank test                |
| 19     | One-way ANOVA                            |
| 20     | Welch's ANOVA                            |
| 21     | Repeated measures ANOVA                  |
| 22     | Kruskal-Wallis test                      |
| 23     | Friedman test                            |


# Usage of `automatical_test()`

The `automatical_test()` function can be used with both individual vectors or a data frame. It automatically selects the most suitable statistical test based on the data provided.

## Example 1: Using Individual Vectors

In this example, we will use two vectors: `Species` and `Sepal.Length` from the `iris` dataset. We will use the `automatical_test()` function to automatically choose the best statistical test for these vectors.

```{r}
# Load the package
library(automatedtests)

# Example 1: Using individual vectors from the iris dataset
test1 <- automatical_test(iris$Species, iris$Sepal.Length, identifiers = FALSE)

# View the result summary
print(test1$get_result())
```

In this case, the function automatically selects the best statistical test based on the data's distribution and other characteristics.

## Example 2: Forcing a Paired Test

Here, we simulate a before-and-after scenario, where data is collected before and after an intervention. The `automatical_test()` function can be instructed to use paired tests by setting the `paired` argument to `TRUE`.

```{r}
# Example 2: Forcing a paired test
before <- c(200, 220, 215, 205, 210)
after <- c(202, 225, 220, 210, 215)
paired_data <- data.frame(before, after)

# Perform the paired test
test2 <- automatical_test(before, after, paired = TRUE)

# View the result summary
print(test2$get_result())
```

By setting `paired = TRUE`, the function forces the use of a paired statistical test, even if identifiers are not provided.

## Example 3: One-Sample Test with Custom Compare Value

You can override the default `compare_to` value to perform one-sample tests. For example, you can test whether the data differs significantly from a specified value.

```{r}
# Example 3: One-sample test
test3 <- automatical_test(iris$Sepal.Length, compare_to = 5)

# View the result summary
print(test3$get_result()$p.value)
```

In this case, `compare_to = 5` specifies that we are performing a one-sample test where we compare the `Sepal.Length` to the value 5.

# Conclusion

The `automatical_test()` function simplifies the process of selecting and running statistical tests. It automatically picks the most appropriate test based on the data's structure and characteristics. You can fine-tune its behavior with options like `compare_to`, `identifiers`, and `paired`.

For more detailed information on the results of each test, you can use the `get_result()` method to retrieve a summary of the test performed.

# See Also

- `AutomatedTest` class for the object returned by the `automatical_test()` function.
- `automatedtests` package documentation.

