NAP.RdCalculates the non-overlap of all pairs index (Parker & Vannest, 2009).
NAP( score, n_a, improvement = "increase", SE = TRUE, CI = TRUE, confidence = 0.95 )
| score | vector of numeric data for A and B phase. Missing values are dropped. |
|---|---|
| n_a | number of observations in Phase A. |
| improvement | character string indicating direction of improvement. Default is "increase" |
| SE | logical value indicating whether to report the standard error |
| CI | logical value indicating whether to report a confidence interval |
| confidence | confidence level for the reported interval estimate |
A list containing the estimate, standard error, and/or confidence interval.
NAP is calculated as the proportion of all pairs of one observation from each phase in which the measurement from the B phase improves upon the measurement from the A phase, with pairs of data points that are exactly tied being given a weight of 0.5. The range of NAP is 0,1, with a null value of 0.5.
The standard error of NAP is calculated based on the method of Hanley and McNeil (1982).
The confidence interval for NAP is calculated based on the symmetrized score-inversion method (Method 5) proposed by Newcombe (2006).
Hanley, J. A., & McNeil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143, 29--36. doi:10.1148/radiology.143.1.7063747
Newcombe, R. G. (2006). Confidence intervals for an effect size measure based on the Mann-Whitney statistic. Part 2: Asymptotic methods and evaluation. Statistics in Medicine, 25(4), 559--573. doi:10.1002/sim.2324
Parker, R. I., & Vannest, K. J. (2009). An improved effect size for single-case research: Nonoverlap of all pairs. Behavior Therapy, 40(4), 357--67. doi:10.1016/j.beth.2008.10.006
A <- c(20, 20, 26, 25, 22, 23) B <- c(28, 25, 24, 27, 30, 30, 29) AB <- c(A,B) NAP(score = AB, n_a = 6)#> $Est #> [1] 0.9166667 #> #> $SE #> [1] 0.07739185 #> #> $CI #> lower upper #> 0.5973406 0.9860176 #># Example from Parker & Vannest (2009) yA <- c(4, 3, 4, 3, 4, 7, 5, 2, 3, 2) yB <- c(5, 9, 7, 9, 7, 5, 9, 11, 11, 10, 9) yAB <- c(yA,yB) NAP(score = yAB, n_a = 6)#> $Est #> [1] 0.7666667 #> #> $SE #> [1] 0.1050279 #> #> $CI #> lower upper #> 0.4870424 0.9128502 #>