| pauc {tcftt} | R Documentation |
Power-adjustment based on non-parametric estimation of the ROC curve
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)