---
title: "Using chaid_table"
author: "Chuck Powell"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Using chaid_table}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, echo = FALSE, warning=FALSE, message=FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

**Chi-square automatic interaction detection (CHAID)** is a decision tree
technique, based on adjusted significance testing (Bonferroni testing). The
technique was developed in South Africa and was published in 1980 by Gordon V.
Kass, who had completed a PhD thesis on this topic. [Wikipedia](https://en.wikipedia.org/wiki/Chi-square_automatic_interaction_detection) 

I've [written a few blog posts](https://ibecav.netlify.app/tags/chaid/) about
using this venerable technique. The default output using `print()` and `plot()`
is sparse, elegant and useful as is, and can be adjusted in many ways that [I
have documented here](https://ibecav.github.io/chaidtutor1/).

The code chunk below is taken straight from the example in the help page for
`CHAID::chaid`

> The dataset is based on data from a post-election survey on persons who voted for either Bush or Gore in the 2000 U.S. election. The specific variables are related to the publication of Magidson and Vermunt (2005).

> Further information (and datasets) about the 2000 U.S. election and other National Election Studies is available on the American National Election Studies Web site (https://electionstudies.org/).


```{r one, fig.width=7.0, fig.height=5.5, warning=FALSE, message=FALSE}
library(CGPfunctions)
# library(CHAID)
library(dplyr)
library(knitr)

### fit tree to subsample see ?chaid
##  set.seed(290875)
##  USvoteS <- USvote[sample(1:nrow(USvote), 1000),]

##  ctrl <- chaid_control(minsplit = 200, minprob = 0.1)
##  chaidUS <- chaid(vote3 ~ ., data = USvoteS, control = ctrl)
print(chaidUS) 
plot(chaidUS)
```

## Getting more detailed

But what happens if you want to investigate your results more closely? I
actually had at least one reader of my blog posts point out that [some other
statistical
packages](https://www.ibm.com/support/knowledgecenter/en/SSLVMB_23.0.0/spss/tutorials/tree_credit_treetable.html#tree_credit_treetable)
produce more detailed information in a tabular format.

Some additional things you may want to know that are actually contained in
`chaidUS` but aren't especially easy to see from the default plot and print and
aren't necessarily easy to ferret out of the `chaidUS` object:

1. It's clear from our call that we fed `chaid` 1,000 random rows of the
   dataset `USvote`. It must be safe to assume that's how many valid cases there were
   right?  The documentation is mute on how it handles missing cases.

2. There's information about counts and frequencies in the terminal nodes (3, 4,
   6, 8, 9) and we could manually calculate the answers to other questions like
   how many people voted for Bush in node #9 (115 * 40.9% = 47)? How many total
   voters are in node #2 (311 + 249 = 560)? But getting more information gets
   increasingly tedious. At node #2 what was the breakdown of votes for Bush -v-
   Gore (hint 245 -v- 315 respectively).

3. It would also be nice to have easy access to the results of the $\chi^2$
   tests that are the inherent workhorse of CHAID. We know that `marstat` was
   selected as the first split by virtue of having the smallest `p value` after
   a Bonferroni adjustment, but what were the results?

`chaid_table` attempts to provide much more granular information in a tibble and
also make it possible for you to derive even more nuanced questions through
piping operations. The simplest call is just to feed it the name of the object
after it has been processed by `CHAID`. That object will be of class
"constparty" "party".

```{r simple2, fig.width=7.0, fig.height=3.5}
# simplest use --- chaidUS is included in the package
class(chaidUS)

chaid_table(chaidUS)

mychaidtable <- chaid_table(chaidUS)
```

I debated the wisdom of providing tables as output in the manner I found most
useful and aesthetically pleasing but in the end decided to simply provide the
tibble and let the user decide what and how to format.

## Example uses

Some easy examples using `kable` and `dplyr`.

```{r simple3}
mychaidtable %>% 
  select(nodeID:ruletext) %>% 
  kable()

# Just node #2 show percentage
mychaidtable %>% 
  select(nodeID:ruletext) %>% 
  filter(nodeID == 2) %>% 
  mutate(pctBush = Bush/NodeN * 100) %>%
  kable(digits = 1)

# Just the children of node #5
mychaidtable %>% 
  select(nodeID:ruletext) %>% 
  filter(parent == 5) %>% 
  kable()

# stats for all splits including raw (unadjusted) p value
mychaidtable %>% 
  select(nodeID, NodeN, split.variable:rawpvalue) %>% 
  filter(!is.na(split.variable)) %>% 
  kable()

```

Hopefully those are enough examples to get your creative juices going.

### Leaving Feedback
If you like CGPfunctions, please consider Filing a GitHub issue by [leaving
feedback here](https://github.com/ibecav/CGPfunctions/issues), or by contacting
me at ibecav at gmail.com by email.

I hope you've found this useful. I am always open to comments, corrections and
suggestions.

Chuck 

### License
<a rel="license" href="https://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.

