calibrateP computes calibrated p-values using the fitted null distribution

calibrateP(null, logRr, seLogRr, twoSided = TRUE, upper = TRUE, ...)

# S3 method for null
calibrateP(null, logRr, seLogRr, twoSided = TRUE, upper = TRUE, ...)

# S3 method for mcmcNull
calibrateP(
null,
logRr,
seLogRr,
twoSided = TRUE,
upper = TRUE,
pValueOnly,
...
)

## Arguments

null

An object of class null created using the fitNull function or an object of class mcmcNull created using the fitMcmcNull function.

logRr

A numeric vector of one or more effect estimates on the log scale

seLogRr

The standard error of the log of the effect estimates. Hint: often the standard error = (log(<lower bound 95 percent confidence interval>) - log(<effect estimate>))/qnorm(0.025)

twoSided

Compute two-sided (TRUE) or one-sided (FALSE) p-value?

upper

If one-sided: compute p-value for upper (TRUE) or lower (FALSE) bound?

...

pValueOnly

If true, will return only the calibrated P-value itself, not the credible interval.

## Value

The calibrated p-value.

## Details

This function computes a calibrated two-sided p-value as described in Schuemie et al (2014).

## Methods (by class)

• null: Computes the calibrated P-value using asymptotic assumptions.

• mcmcNull: Computes the calibrated P-value and 95 percent credible interval using Markov Chain Monte Carlo (MCMC).

## Examples

data(sccs)
negatives <- sccs[sccs$groundTruth == 0, ] null <- fitNull(negatives$logRr, negatives$seLogRr) positive <- sccs[sccs$groundTruth == 1, ]
calibrateP(null, positive$logRr, positive$seLogRr)
#>  0.839405