Conducts a G-test of independence to assess the goodness of fit or the association between two categorical variables in a 2x2 contingency table. It is an alternative to the chi-squared test and is particularly useful when dealing with small 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.
Details
The test compares the observed frequencies to the expected frequencies based on the marginal totals and calculates a G statistic, which follows a chi-squared distribution. The test is especially useful when the data contains small expected frequencies, which might make the chi-squared test less accurate. A low p-value indicates a significant association between the variables or a significant difference from the expected distribution. Caution is needed with zero counts or very small samples.
Examples
cm <- dx_cm(dx_heart_failure$predicted, dx_heart_failure$truth,
threshold = 0.3, poslabel = 1
)
simple <- dx_g_test(cm, detail = "simple")
detailed <- dx_g_test(cm)
print(simple)
#> [1] 5.50871e-23
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 G-Test p<0.01 5.51e-23 NA NA "" "" ""