Scoring via the chi-squared test or Fisher's exact test

These two objects can be used to compute importance scores based on chi-squared test or Fisher's exact test.

Usage

score_xtab_pval_chisq

score_xtab_pval_fisher

Format

An object of class filtro::class_score_xtab (inherits from filtro::class_score, S7_object) of length 1.

Value

An S7 object. The primary property of interest is in results. This is a data frame of results that is populated by the fit() method and has columns:

name: The name of the score (e.g., pval_chisq).
score: The estimates for each predictor.
outcome: The name of the outcome column.
predictor: The names of the predictor inputs.

These data are accessed using object@results (see examples below).

Details

These objects are used when:

The predictors are factors and the outcome is a factor.

In this case, a contingency table (via table()) is created with the proper variable roles, and the cross tabulation p-value is computed using either the chi-squared test (via stats::chisq.test()) or Fisher's exact test (via stats::fisher.test()). The p-value that is returned is transformed to be -log10(p_value) so that larger values are associated with more important predictors.

Estimating the scores

In filtro, the score_* objects define a scoring method (e.g., data input requirements, package dependencies, etc). To compute the scores for a specific data set, the fit() method is used. The main arguments for these functions are:

object: A score class object (e.g., score_xtab_pval_chisq).
formula: A standard R formula with a single outcome on the right-hand side and one or more predictors (or .) on the left-hand side. The data are processed via stats::model.frame()
data: A data frame containing the relevant columns defined by the formula.
...: Further arguments passed to or from other methods.
case_weights: A quantitative vector of case weights that is the same length as the number of rows in data. The default of NULL indicates that there are no case weights.

Missing values are removed for each predictor/outcome combination being scored.

In cases where the underlying computations fail, the scoring proceeds silently, and a missing value is given for the score.

Examples

# Binary factor example

library(titanic)
library(dplyr)

titanic_subset <- titanic_train |>
  mutate(across(c(Survived, Pclass, Sex, Embarked), as.factor)) |>
  select(Survived, Pclass, Sex, Age, Fare, Embarked)

# Chi-squared test
titanic_xtab_pval_chisq_res <- score_xtab_pval_chisq |>
  fit(Survived ~ ., data = titanic_subset)
titanic_xtab_pval_chisq_res@results
#> # A tibble: 5 × 4
#>   name            score outcome  predictor
#>   <chr>           <dbl> <chr>    <chr>    
#> 1 xtab_pval_chisq 22.3  Survived Pclass   
#> 2 xtab_pval_chisq 57.9  Survived Sex      
#> 3 xtab_pval_chisq NA    Survived Age      
#> 4 xtab_pval_chisq NA    Survived Fare     
#> 5 xtab_pval_chisq  5.79 Survived Embarked 

# Fisher's exact test
titanic_xtab_pval_fisher_res <- score_xtab_pval_fisher |>
  fit(Survived ~ ., data = titanic_subset)
titanic_xtab_pval_fisher_res@results
#> # A tibble: 5 × 4
#>   name             score outcome  predictor
#>   <chr>            <dbl> <chr>    <chr>    
#> 1 xtab_pval_fisher 22.5  Survived Pclass   
#> 2 xtab_pval_fisher 59.2  Survived Sex      
#> 3 xtab_pval_fisher NA    Survived Age      
#> 4 xtab_pval_fisher NA    Survived Fare     
#> 5 xtab_pval_fisher  5.99 Survived Embarked 

# Chi-squared test where `class` is the multiclass outcome/response

hpc_subset <- modeldata::hpc_data |>
  dplyr::select(
    class,
    protocol,
    hour
  )

hpc_xtab_pval_chisq_res <- score_xtab_pval_chisq |>
    fit(class ~ ., data = hpc_subset)
hpc_xtab_pval_chisq_res@results
#> # A tibble: 2 × 4
#>   name             score outcome predictor
#>   <chr>            <dbl> <chr>   <chr>    
#> 1 xtab_pval_chisq  0.246 class   protocol 
#> 2 xtab_pval_chisq NA     class   hour