pauc | R Documentation |
It is common to use Monte Carlo experiments to evaluate the performance of hypothesis tests and compare the empirical power among competing tests. High power is desirable but difficulty arises when the actual sizes of competing tests are not comparable. A possible way of tackling this issue is to adjust the empirical power according to the actual size. This function implements the "method 2: non-parametric estimation of the ROC curve" in Lloyd (2005). For more details, please refer to the paper.
pauc(stat_h0, stat_ha, target_range_lower, target_range_upper)
stat_h0 |
simulated test statistics under the null hypothesis. |
stat_ha |
simulated test statistics under the alternative hypothesis. |
target_range_lower |
the lower end of the size range. |
target_range_upper |
the upper end of the size range. |
the adjusted power.
Lloyd, C. J. (2005). Estimating test power adjusted for size. Journal of Statistical Computation and Simulation, 75(11):921-933.
stath0 <- rnorm(100)
statha <- rnorm(100, mean=1)
pauc(stath0, statha, 0.01, 0.1)
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