# Class "GBH"

### Description

Class to facilitate performing the Group Benjamini-Hochberg procedure and interpreting its output.

### Slots

`p.vals`

:Object of class

`"data.frame"`

. Each row correpsonds to an individual hypothesis. The first column stores the p-values before GBH adjustment, while the second gives the GBH adjusted p-values. The hypotheses are sorted in order of significance according to these GBH adjusted p-values. The`group`

column gives the group membership of each hypothesis, and`adj.significnace`

codes the significance of each hypothesis, according to the GBH adjusted p-values.`pi0`

:Object of class

`"numeric"`

. The proportion of null hypotheses within each group. This is either known a priori or estimated adaptively from the unadjusted p-values.`adaptive`

:Object of class

`"logical"`

. An indicator of whether the proportion`pi0`

was estimated adaptively from the data or known a priori.`alpha`

:Object of class

`"numeric"`

. The level at which the FDR is controlled, during the GBH procedure.

### Methods

- plot
`signature(x = "GBH", y = "ANY")`

: ...

Plots the p-values of the hypothesis, sorted according to GBH adjusted significance, shape coded according to group membership, and color coded according to pre and post GBH p-value adjustment.

- show
`signature(object = "GBH")`

: ...

Prints the entire table of adjusted p-values and their associated FDR adjusted significance levels, together with the estimated proportions of null hypotheses, within each group.

- summary
`signature(object = "GBH")`

: ...

Prints the most significant hypothesis, after adjusting for multiple testing via GBH. Also supplies the estimated proportion of null hypothesis within each group and a table of counts of adjusted significance across groups.

### Author(s)

Kris Sankaran

### References

Hu, J.X., Zhao, H., and Zhou, H.H. False discovery rate control with groups. Journal of the American Statistical Association, volume 104, number 491. Pages 1215-1227. 2010.

Sankaran, K and Holmes, S. structSSI: Simultaneous and Selective Inference for Grouped or Hierarchically Structured Data. Journal of Statistical Software, 59(13), 1-21. 2014. http://jstatsoft.org/v59/i13/

### See Also

`Adaptive.GBH`

`Oracle.GBH`

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ```
## These are the unadjusted p-values corresponding to
## the outcome of some multiple testing experiment. The
## first 500 hypotheses are null and the last 1500 are
## true alternatives.
unadjp <- c(runif(500, 0, 0.01), runif(1500, 0, 1))
names(unadjp) <- paste("Hyp: ", 1:2000)
## These are the unadjusted p-values corresponding to
## the outcome of some multiple testing experiment. The
## first 500 hypotheses are null and the last 1500 are
## true alternatives.
unadjp <- c(runif(500, 0, 0.01), runif(1500, 0, 1))
names(unadjp) <- paste("Hyp: ", 1:2000)
## Here there are two groups total we have randomly
## assigned hypotheses to these two groups.
group.index <- c(sample(1:2, 2000, replace = TRUE))
# Perform the GBH adjustment.
result <- Adaptive.GBH(unadjp, group.index, method = "storey")
# A summary of the GBH adjustment
summary(result)
``` |