pCalibrate: Calibrations of two-sided p-values directly based on the...

In pCalibrate: Bayesian Calibrations of p-Values

Description

Transforms a two-sided p-value to a minimum Bayes factor. That minimum Bayes factor is obtained by modelling the distributions of the p-value under the null and the alternative hypothesis, respectively.

Usage

pCalibrate(p,  type="exploratory", transform="id")

Arguments

p	a vector of two-sided p-values
type	either "exploratory" or "confirmatory". Defaults to "exploratory". Corresponds to different distrbutional assumptions for the p-value p under the alternative.
transform	either "id", "log", "log2" or "log10". Defaults to "id". Specifies how to transform the minimum Bayes factor(s). "id" corresponds to no transformation. "log" refers to the natural logarithm, "log2" to the logarithm to the base 2 and "log10" to the logarithm to the base 10.

Details

If type="exploratory" is used, the so-called "- e p log(p)" calibration (Sellke et al., 2001) is applied.

If type="confirmatory" is used, the the so-called "- e q log(q)" calibration with q=1-p (Held & Ott, 2018) is applied.

type="exploratory" gives a larger minimum Bayes factor than type="confirmatory".

Under the null hypothesis, the distribution of the p-value is assumed to be uniform. Under the alternative, the p-value is assumed to have a beta-distribution with monotonically decreasing density function under both alternatives, with different parameters however. If type="exploratory", the prior sample size does not exceed 2, whereas for type="confirmatory", the prior sample size is at least 2. The latter calibration may be appropriate for small sample size, but for larger sample size it is too conservative (Held & Ott, 2018).

Note that for the "- e p log(p)" calibration, alternative derivations which do not assume a beta-distribution under the alternative have also been given, see Sellke et al. (2001).

Value

A numeric vector of minimum Bayes factors for the specified p-values

Note

The argument type replaces the argument alternative in version 0.1-1 of this package.
type="exploratory" corresponds to alternative="noninformative" and
type="confirmatory" corresponds to alternative="informative".

References

Held, L. and Ott, M. (2018). On p-values and Bayes factors. Annual Review of Statistics and Its Application, 5, 393–419.

Sellke, T., Bayarri, M. J. and Berger, J. O. (2001). Calibration of p values for testing precise null hypotheses. The American Statistician, 55, 62–71.

Vovk, V. G. (1993). A logic of probability, with application to the foundations of statistics (with discussion and a reply by the author). Journal of the Royal Statistical Society, Series B, 55, 317–351.

Examples

pCalibrate(p=c(0.05, 0.01, 0.001))
pCalibrate(p=c(0.05, 0.01, 0.001), type="confirmatory")

# plot the 2 calibrations as a function of the p-value
par(las=1)
p <- exp(seq(log(0.0001), log(0.3), by=0.01))
minBF1 <- pCalibrate(p=p)
minBF2 <- pCalibrate(p=p, type="confirmatory")
plot(p, minBF1, type="l", log="xy",
      xlab="Two sided p-value", ylab="Minimum Bayes factor", 
     axes=FALSE, lwd=2, col=1)
lines(p, minBF2, col=2, lwd=2)
axis(1, at=c(0.0001, 0.0003, 0.001, 0.003, 0.01, 0.03, 0.1, 0.3), 
     as.character(c(format(c(0.0001,0.0003), nsmall=4, digits=4, 
                  scientific=FALSE), 
     c(0.001, 0.003, 0.01, 0.03, 0.1, 0.3))))
my.values <- c(3000, 1000, 300, 100, 30, 10, 3, 1)
my.at <- 1/my.values
my.ylegend <- c(paste("1/", my.values[-length(my.values)], sep=""), "1")
axis(2, at=my.at, my.ylegend)
box()
legend("bottomright", lty=1, lwd=2, 
        legend=c("- e p log(p)", "- e q log(q)")
                , col=c(1,2))

pCalibrate documentation built on March 23, 2020, 3:01 a.m.