PRMmcp: Monte Carlo power calculation for PRM design problem

In baolinwu/SPprm: Statistical methods with power calculation for paired repeated measures comparisons: application to pulse oximetry studies

Description

Due to its nature of composite null hypothesis, it is not trivial to derive the analytical power calculation. Monte Carlo simulation offers a straightforward approach by generating p-value random numbers.

Usage

PRMmcp(alpha = 0.05, n, m, s2w, s2b, mu, rho, REML = TRUE, B = 1000)

Arguments

alpha	desired significance level. Default to 0.05
n	number of subjects
m	number of repeated measures for each subject
s2w	within subject variation
s2b	between subject variation
mu	mean measure difference
rho	null threshold of acceptable RMS value
REML	using REML instead of MLE. Default to TRUE.
B	total number of Monte Carlo simulations. Default to 1000.

Value

pwr

computed power

p.value

p-values from Monte Carlo simulations

References

Bai,Y., Wang,Z., Lystig,T.C., and Wu,B. (2018) Statistical test with sample size and power calculation for paired repeated measures designs of method comparison studies.

baolinwu/SPprm documentation built on May 9, 2019, 8:41 a.m.