calculateSwfdr {swfdr} | R Documentation |
Calculate the science-wise FDR (swfdr)
calculateSwfdr(pValues, truncated, rounded, pi0 = 0.5, alpha = 1, beta = 50, numEmIterations = 100)
pValues |
Numerical vector of p-values |
truncated |
Vector of 0s and 1s with indices corresponding to those in pValues; 1 indicates that the p-values is truncated, 0 that it is not truncated |
rounded |
Vector of 0s and 1s with indices corresponding to those in pValues; 1 indicates that the p-values is rounded, 0 that it is not rounded |
pi0 |
Initial prior probability that a hypothesis is null (default is 0.5) |
alpha |
Initial value of parameter alpha from Beta(alpha, beta) true positive distribution (default is 1) |
beta |
Initial value of parameter beta from Beta(alpha, beta) true positive distribution (default is 50) |
numEmIterations |
The number of EM iterations (default is 100) |
pi0 Final value of prior probability - estimated from EM - that a hypothesis is null, i.e. estimated swfdr
alpha Final value of parameter alpha - estimated from EM - from Beta(alpha, beta) true positive distribution
beta Final value of parameter beta - estimated from EM - from Beta(alpha, beta) true positive distribution
z Vector of expected values of the indicator of whether the p-value is null or not - estimated from EM - for the non-rounded p-values (values of NA represent the rounded p-values)
n0 Expected number of rounded null p-values - estimated from EM - between certain cutpoints (0.005, 0.015, 0.025, 0.035, 0.045, 0.05)
n Number of rounded p-values between certain cutpoints (0.005, 0.015, 0.025, 0.035, 0.045, 0.05)
pVals <- runif(100) tt <- rr <- rep(0, 100) resSwfdr <- calculateSwfdr(pValues = pVals, truncated = tt, rounded = rr, numEmIterations=100)