McNemar's Test with Plain-English Interpretation

Usage

mcnemar_interpret(x, y, conf.level = 0.95, context = NULL)

Arguments

x: A factor or character vector (first measurement)
y: A factor or character vector (second measurement)
conf.level: Confidence level. Default 0.95.
context: Optional description of the study design or sampling method, echoed back in the printed report. Default NULL.

Value

An object of class statease_mcnemar containing test results and interpretation. Use print() to display the formatted report.

Examples

x <- c("Yes","No","Yes","Yes","No","Yes","No","No","Yes","Yes")
y <- c("No","No","Yes","Yes","No","Yes","Yes","No","Yes","No")
result <- mcnemar_interpret(x, y)
print(result)
#> 
#> statease McNemar's Test Report --------------------------------
#>   N            : 10
#>   Table size   : 2 x 2
#>   Discordant   : 3 pairs
#> -----------------------------------------------------------------
#>   Contingency Table:
#>      y
#> x     No Yes
#>   No   3   1
#>   Yes  2   4
#> 
#> -----------------------------------------------------------------
#>   p-value      : 1.0000
#>   Matched OR   : 0.500
#>   95% CI      : [0.045, 5.514]
#> -----------------------------------------------------------------
#>   Assumption Checks:
#>     Discordant pairs (b+c)    : WARNING  (n = 3, small for asymptotic test)
#>     Sample independence       : NOTE     (assumed from study design, not testable from data)
#> 
#>   NOTE: Assumption checks are diagnostic tools and may be
#>   influenced by sample size and other characteristics of the
#>   data. Passing a check does not prove that an assumption is
#>   satisfied, and a warning does not automatically invalidate
#>   the analysis. Interpret these results alongside your
#>   knowledge of the data.
#> -----------------------------------------------------------------
#>   Interpretation:
#>   The result is not statistically significant (p = 1.0000 > alpha 0.05).
#>   There is insufficient evidence of a significant difference in paired proportions between the two measurements.
#>   Matched OR = 0.500: More subjects changed from the second category to the first category than vice versa.
#>   95% CI [0.045, 5.514] includes 1: No statistically significant evidence of a difference in paired proportions.
#> 
#>   WARNING: The number of discordant pairs is very small (less than 10). Results may be unreliable. Interpret with caution.
#>   WARNING: McNemar's Test assumes that observations are paired and independent across pairs. Violation of this assumption may affect the validity of the results.
#> 
#>   NOTE: McNemar's Test requires paired or matched data. Ensure that each row in your data represents the same subject measured twice or a matched pair.
#> -----------------------------------------------------------------
#>