Conducts Fisher's Exact test of independence on a 2x2 confusion matrix derived from binary classification results, assessing the significance of the association between the observed and expected frequencies.
Arguments
- cm
A dx_cm object created by
dx_cm().- detail
Character specifying the level of detail in the output: "simple" for raw estimate, "full" for detailed estimate including 95% confidence intervals.
Value
Depending on the detail parameter:
- if "simple": a single numeric value representing the p-value of
Fisher's Exact test.
- if "full": a data frame with the Fisher's Exact test result, including
the p-value and method note.
Details
Fisher's Exact Test is used to examine the significance of the association between the variables in a 2x2 contingency table, particularly useful when sample sizes are small. Unlike the chi-square test, it does not rely on large sample distribution approximations and is hence exact. It's especially preferred when the data has small expected frequencies in one or more cells of the table. A low p-value indicates a significant association between the predicted and actual binary classifications.
See also
dx_cm() for creating a 'dx_cm' object.
Examples
cm <- dx_cm(dx_heart_failure$predicted, dx_heart_failure$truth,
threshold = 0.3, poslabel = 1
)
simple <- dx_fishers_exact(cm, detail = "simple")
detailed <- dx_fishers_exact(cm)
print(simple)
#> [1] 1.660279e-22
print(detailed)
#> # A tibble: 1 × 8
#> measure summary estimate conf_low conf_high fraction conf_type notes
#> <chr> <chr> <dbl> <lgl> <lgl> <chr> <chr> <chr>
#> 1 Fisher's Exact p<0.01 1.66e-22 NA NA "" "" Exact t…