Description Slots Methods Author(s) References See Also Examples

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

`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.

- 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.

Kris Sankaran

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/

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)
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.