threshold.select {bootcluster} R Documentation

## Estimate of the overall Jaccard stability

### Description

Estimate of the overall Jaccard stability

### Arguments

 `data.input` a `data.frame` of the data set where the rows are observations and columns are covariates `threshold.seq` a `numeric` sequence of candidate threshold `B` number of bootstrap re-samplings `cor.method` the correlation method applied to the data set,three method are available: `"pearson", "kendall", "spearman"`. `large.size` the smallest set of modules, the `large.size=0` is recommended to use right now. `PermuNo` number of random graphs for the estimation of expected stability `no_cores` a `interger` number of CPU cores on the current host (This function can't not be used yet).

### Details

`threshold.select` is used to estimate of the overall Jaccard stability from a sequence of given threshold candidates, `threshold.seq`.

### Value

`stabilityresult`

a `list` of result for nodes-wise stability

`modularityresult`

a `list` of modularity information with each candidate threshold

`jaccardresult`

a `list` estimated unconditional observed stability and the estimates of expected stability under the nul

`originalinformation`

a `list` information for original data, igraph object and adjacency matrix constructed with each candidate threshold

`threshold.seq`

a `list` of candicate threshold given to the function

Mingmei Tian

### References

A framework for stability-based module detection in correlation graphs. Mingmei Tian,Rachael Hageman Blair,Lina Mu, Matthew Bonner, Richard Browne and Han Yu.

### Examples

```
set.seed(1)
data(wine)
x0 <- wine[1:50,]

mytest<-threshold.select(data.input=x0,threshold.seq=seq(0.5,0.8,by=0.05), B=20,
cor.method='pearson',large.size=0,
PermuNo = 10,
no_cores=1,
scheme_2 = FALSE)

```

[Package bootcluster version 0.2.5 Index]