fdrisk: False Discovery Risk for Second-Generation p-Values
In weltybiostat/sgpv: Calculate Second-Generation p-Values and Associated Measures

Description Usage Arguments Details Value References See Also Examples

View source: R/fdrisk.R

This function computes the false discovery risk (sometimes called the "empirical bayes FDR") for a second-generation p-value of 0, or the false confirmation risk for a second-generation p-value of 1.

fdrisk(
  sgpval = 0,
  null.lo,
  null.hi,
  std.err,
  interval.type,
  interval.level,
  pi0 = 0.5,
  null.weights,
  null.space,
  alt.weights,
  alt.space
)

`sgpval`	The observed second-generation p-value. Default is 0, which gives the false discovery risk.
`null.lo`	The lower bound of the indifference zone (null interval) upon which the second-generation p-value was based
`null.hi`	The upper bound for the indifference zone (null interval) upon which the second-generation p-value was based
`std.err`	Standard error of the point estimate
`interval.type`	Class of interval estimate used. This determines the functional form of the power function. Options are `confidence` for a (1-α)100% confidence interval and `likelihood` for a 1/k likelihood support interval (`credible` not yet supported).
`interval.level`	Level of interval estimate. If `interval.type` is `confidence`, the level is α. If `interval.type` is `likelihood`, the level is 1/k (not k).
`pi0`	Prior probability of the null hypothesis. Default is 0.5.
`null.weights`	Probability distribution for the null parameter space. Options are currently `Point`, `Uniform`, and `TruncNormal`.
`null.space`	Support of the null probability distribution. If `null.weights` is `Point`, then `null.space` is a scalar. If `null.weights` is `Uniform`, then `null.space` is a vector of length two.
`alt.weights`	Probability distribution for the alternative parameter space. Options are currently `Point`, `Uniform`, and `TruncNormal`.
`alt.space`	Support for the alternative probability distribution. If `alt.weights` is `Point`, then `alt.space` is a scalar. If `alt.weights` is `Uniform`, then `alt.space` is a vector of length two.

When possible, one should compute the second-generation p-value and FDR/FCR on a scale that is symmetric about the null hypothesis. For example, if the parameter of interest is an odds ratio, inputs pt.est, std.err, null.lo, null.hi, null.space, and alt.space are typically on the log scale.

If TruncNormal is used for null.weights, then the distribution used is a truncated Normal distribution with mean equal to the midpoint of null.space, and standard deviation equal to std.err, truncated to the support of null.space. If TruncNormal is used for alt.weights, then the distribution used is a truncated Normal distribution with mean equal to the midpoint of alt.space, and standard deviation equal to std.err, truncated to the support of alt.space. Further customization of these parameters for the truncated Normal are currently not possible, although they may be implemented in future versions.

Numeric scalar representing the False discovery risk (FDR) or false confirmation risk (FCR) for the observed second-generation p-value. If sgpval = 0, the function returns false discovery risk (FDR). If sgpval = 1, the function returns false confirmation risk (FCR).

Blume JD, Greevy RA Jr., Welty VF, Smith JR, Dupont WD (2019). An Introduction to Second-generation p-values. The American Statistician. 73:sup1, 157-167, DOI: https://doi.org/10.1080/00031305.2018.1537893

Blume JD, D’Agostino McGowan L, Dupont WD, Greevy RA Jr. (2018). Second-generation p-values: Improved rigor, reproducibility, & transparency in statistical analyses. PLoS ONE 13(3): e0188299. https://doi.org/10.1371/journal.pone.0188299

sgpvalue, sgpower, plotsgpv, FDRestimation::p.fdr

# false discovery risk with 95% confidence level
fdrisk(sgpval = 0,  null.lo = log(1/1.1), null.hi = log(1.1),  std.err = 0.8,
  null.weights = 'Uniform', null.space = c(log(1/1.1), log(1.1)),
  alt.weights = 'Uniform',  alt.space = 2 + c(-1,1)*qnorm(1-0.05/2)*0.8,
  interval.type = 'confidence',  interval.level = 0.05)

# false discovery risk with 1/8 likelihood support level
fdrisk(sgpval = 0,  null.lo = log(1/1.1), null.hi = log(1.1),  std.err = 0.8,
  null.weights = 'Point', null.space = 0,  alt.weights = 'Uniform',
  alt.space = 2 + c(-1,1)*qnorm(1-0.041/2)*0.8,
  interval.type = 'likelihood',  interval.level = 1/8)

## with truncated normal weighting distribution
fdrisk(sgpval = 0,  null.lo = log(1/1.1), null.hi = log(1.1),  std.err = 0.8,
  null.weights = 'Point', null.space = 0,  alt.weights = 'TruncNormal',
  alt.space = 2 + c(-1,1)*qnorm(1-0.041/2)*0.8,
  interval.type = 'likelihood',  interval.level = 1/8)

# false discovery risk with LSI and wider null hypothesis
fdrisk(sgpval = 0,  null.lo = log(1/1.5), null.hi = log(1.5),  std.err = 0.8,
  null.weights = 'Point', null.space = 0,  alt.weights = 'Uniform',
  alt.space = 2.5 + c(-1,1)*qnorm(1-0.041/2)*0.8,
  interval.type = 'likelihood',  interval.level = 1/8)

# false confirmation risk example
fdrisk(sgpval = 1,  null.lo = log(1/1.5), null.hi = log(1.5),  std.err = 0.15,
  null.weights = 'Uniform', null.space = 0.01 + c(-1,1)*qnorm(1-0.041/2)*0.15,
  alt.weights = 'Uniform',  alt.space = c(log(1.5), 1.25*log(1.5)),
  interval.type = 'likelihood',  interval.level = 1/8)