calculateSwfdr {swfdr} | R Documentation

## Calculate the science-wise FDR (swfdr)

### Description

Calculate the science-wise FDR (swfdr)

### Usage

    calculateSwfdr(pValues, truncated, rounded, pi0 = 0.5, alpha = 1,
      beta = 50, numEmIterations = 100)

### Arguments

`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)

### Value

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)

### Examples

    pVals <- runif(100)
    tt <- rr <- rep(0, 100)
    resSwfdr <- calculateSwfdr(pValues = pVals, truncated = tt, rounded = rr, numEmIterations=100)

[Package swfdr version 1.6.0 Index]