Description Usage Arguments Value Author(s) References Examples

Given a set of p-values, return the decisions using the generalized stepwise procedure.

1 |

`tree` |
the edgelist parameterizing the hierarchical structure between hypotheses. The edges must be stored so that each edge is a row of a two column matrix, where the first column gives the parent and the second gives the child. |

`pvals` |
a vector of raw p-values resulting from an experiment. The names of this vector should be contained in the edgelist parameterizing the hierarchical structure between hypothesis, inputted as |

`alpha` |
the significant level used to calculate the critical values to make decisions. |

`type` |
the type of dependence structure of the hierarchically ordered hypotheses. Currently, we provide four types of dependence: |

logical values of each hypothesis being rejected or not, if `TRUE`

, then the hypothesis is rejected; otherwise, the hypothesis is not rejected.

Yalin Zhu

Lynch, G., Guo, W. (2016).
On Procedures Controlling the FDR for Testing Hierarchically Ordered Hypotheses.
*arXiv preprint* arXiv:1612.04467.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
library(igraph)
library(ape)
library(structSSI)
library(phyloseq)
data("chlamydiae")
environments <- sample_data(chlamydiae)$SampleType
abundances <- otu_table(chlamydiae)
graph.tree <- as.igraph(phy_tree(chlamydiae))
edge.tree <- get.edgelist(graph.tree)
pVal <- treePValues(edge.tree, abundances, environments)
pVal[which(is.na(pVal))] = 1; # these have all 0 abundances in every environment
decision1 <- hier.test(tree = graph.tree, pvals = pVal, alpha = 0.01, type = "positive")
decision2 <- hier.test(tree = graph.tree, pvals = pVal, alpha = 0.01, type = "arbitrary")
## show the number of rejections under different types of dependence
length(which(decision1 == TRUE)); length(which(decision2 == TRUE))
``` |

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