Description Usage Arguments Value Author(s) References Examples

Given a list/data frame of grouped p-values, selecting thresholds and p-value combining method, retruns adjusted conditional p-values to make decisions

1 | ```
GBH.p.adjust(pval, t, make.decision)
``` |

`pval` |
the structural p-values, the type should be |

`t` |
the thresholds determining whether the families are selected or not, also affects conditional p-value within families. |

`make.decision` |
logical; if |

A list of the adjusted conditional p-values, a list of `NULL`

means the family is not selected to do the test in the second stage.

Yalin Zhu

Hu, J. X., Zhao, H., & Zhou, H. H. (2010).
False discovery rate control with groups.
*Journal of the American Statistical Association*, **105**: 1215-1227.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ```
# data is from Example 4.1 in Mehrotra and Adewale (2012)
pval <- list(c(0.031,0.023,0.029,0.005,0.031,0.000,0.874,0.399,0.293,0.077),
c(0.216,0.843,0.864),
c(1,0.878,0.766,0.598,0.011,0.864),
c(0.889,0.557,0.767,0.009,0.644),
c(1,0.583,0.147,0.789,0.217,1,0.02,0.784,0.579,0.439),
c(0.898,0.619,0.193,0.806,0.611,0.526,0.702,0.196))
sum(p.adjust(unlist(pval), method = "BH")<=0.1)
DFDR.p.adjust(pval = pval,t=0.1)
DFDR2.p.adjust(pval = pval,t=0.1)
sum(unlist(DFDR.p.adjust(pval = pval,t=0.1))<=0.1)
sum(unlist(DFDR2.p.adjust(pval = pval,t=0.1))<=0.1)
GBH.p.adjust(pval = pval,t=0.1)
sum(unlist(GBH.p.adjust(pval = pval,t=0.1))<=0.1)
t=select.thres(pval,select.method = "BH", comb.method = "minP", alpha = 0.1)
cFDR.cp.adjust(pval, t=t, comb.method="minP")
t1=select.thres(pval, select.method = "bonferroni", comb.method = "minP", alpha = 0.1, k=3)
cFDR.cp.adjust(pval, t=t1, comb.method="minP")
t2=select.thres(pval, select.method = "sidak", comb.method = "minP", alpha = 0.1, k=3)
cFDR.cp.adjust(pval, t=t2, comb.method="minP")
``` |

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