pauc: Power-adjustment based on non-parametric estimation of the...

In tcftt: Two-Sample Tests for Skewed Data

Description

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.

Usage

pauc(stat_h0, stat_ha, target_range_lower, target_range_upper)

Arguments

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.

Value

the adjusted power.

References

Lloyd, C. J. (2005). Estimating test power adjusted for size. Journal of Statistical Computation and Simulation, 75(11):921-933.

Examples

stath0 <- rnorm(100)
statha <- rnorm(100, mean=1)
pauc(stath0, statha, 0.01, 0.1)

tcftt documentation built on July 23, 2020, 5:08 p.m.