Using chaid_table

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 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”.

# simplest use --- chaidUS is included in the package
class(chaidUS)
#> [1] "constparty" "party"

chaid_table(chaidUS)
#> # A tibble: 9 x 15
#>   nodeID parent NodeN  Gore  Bush ruletext split.variable chisq    df adjustedp
#>    <int>  <int> <dbl> <int> <int> <chr>    <chr>          <dbl> <int>     <dbl>
#> 1      1     NA   961   495   466 <NA>     marstat         32.3     1   9.03e-8
#> 2      2      1   560   245   315 marstat… educr           12.9     1   1.33e-3
#> 3      3      2   311   157   154 marstat… <NA>            NA      NA  NA      
#> 4      4      2   249    88   161 marstat… <NA>            NA      NA  NA      
#> 5      5      1   401   250   151 marstat… gender          11.5     1   6.79e-4
#> 6      6      5   159    83    76 marstat… <NA>            NA      NA  NA      
#> 7      7      5   242   167    75 marstat… ager            10.0     1   7.84e-3
#> 8      8      7   127    99    28 marstat… <NA>            NA      NA  NA      
#> 9      9      7   115    68    47 marstat… <NA>            NA      NA  NA      
#> # … with 5 more variables: rawpvalue <dbl>, splitrule <chr>, split1 <chr>,
#> #   split2 <chr>, split3 <chr>

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.

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.