Description Usage Arguments Details Value See Also Examples

Computes the exponential p-value weights for multiple testing.
Given estimated means `mu`

of test statistics `T`

,
the p-value weights are proportional to `exp(beta*mu)`

,
for a tilt parameter `beta`

. In addition, the large weights are truncated
at a maximum value `UB`

(upper bound), and the remaining weight is re-distributed
among the rest of the statistics.

1 | ```
exp_weights(mu, beta = 2, UB = Inf)
``` |

`mu` |
the estimated means of the test statistics |

`beta` |
(optional) weights are proportional to |

`UB` |
(optional) upper bound on the weights (default |

Specifically, it is assumed that `T`

are Gaussian with mean
`mu`

. One-sided tests of `mu>=0`

against `mu<0`

are conducted using the test statistics `T`

. To optimize power,
different levels are allocated to different tests.
For more details, see the paper "Optimal Multiple Testing Under a
Gaussian Prior on the Effect Sizes", by Dobriban, Fortney, Kim and Owen,
http://arxiv.org/abs/1504.02935

The exponential weights.

`bayes_weights`

for Bayes, `spjotvoll_weights`

for Spjotvoll weights, and `exp_weights`

for exponential
weights

Other p.value.weighting: `bayes_weights`

;
`iGWAS`

; `spjotvoll_weights`

1 2 3 4 5 | ```
J <- 100
mu <- rnorm(J)
beta <- 2
UB <- 20
w <- exp_weights(mu, beta, UB)
``` |

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