Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/prob_rank_givenEffect_approx.R

A normal approximation to comnpute the probability of rank of a test being higher than any other test given the effect size from external information.

1 2 | ```
prob_rank_givenEffect_approx(k, et, ey, nrep = 10000, m0, m1,
effectType = c("binary", "continuous"))
``` |

`k` |
Integer, rank of a test |

`et` |
Numeric, effect of the targeted test for importance sampling |

`ey` |
Numeric, mean/median covariate efffect from external information |

`nrep` |
Integer, number of replications for importance sampling |

`m0` |
Integer, number of true null hypothesis |

`m1` |
Integer, number of true alternative hypothesis |

`effectType` |
Character ("continuous" or "binary"), type of effect sizes |

If one wants to test

*H_0: epsilon_i=0 vs. H_a: epsilon_i > 0,*

then `ey`

should be mean of the covariate effect sizes,
This is called hypothesis testing for the continuous effect sizes.

If one wants to test

*H_0: epsilon_i=0 vs. H_a: epsilon_i = epsilon,*

then `ey`

should be median or any discrete value of the
covariate effect sizes. This is called hypothesis testing for the Binary
effect sizes.

`m1`

and `m0`

can be estimated using `qvalue`

from
a bioconductor package `qvalue`

.

`prob`

Numeric, probability of the rank of a test

Mohamad S. Hasan, [email protected]

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
# compute the probability of the rank of a test being third if all tests are
# from the true null
prob <- prob_rank_givenEffect(k = 3, et = 0, ey = 0, nrep = 10000,
m0 = 50, m1 = 50)
# compute the probabilities of the ranks of a test being rank 1 to 100 if the
# targeted test effect is 2 and the overall mean covariate effect is 1.
ranks <- 1:100
prob <- sapply(ranks, prob_rank_givenEffect, et = 2, ey = 1, nrep = 10000,
m0 = 50, m1 = 50)
# plot
plot(ranks,prob)
``` |

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