corBootstrap {ProCoNA} | R Documentation

## corBootstrap

### Description

Boostraps a correlation matrix. In order to bootstrap a large correlation matrix, several thousand samplings may be necessary. To avoid storing thousands of matrices, a running mean is kept for each pairwise correlation. In addition, a running standard deviation is computed so that for each pairwise correlation, we can estimate the distribution of values across resamplings. After each resampling, a new correlation matrix is computed. A difference is taken between this new matrix and the running mean. If all differences are less than the specified threshold, then the bootstrapped matrix has converged to a final state.

### Usage

    corBootstrap(dataMatrix, networkType = "signed", threshold = 1e-04,
        tmpSaveFile = TRUE)

### Arguments

`dataMatrix` | Matrix with samples in rows and peptides (or other data type) in columns.
`networkType` | Whether the sign is considered in constructing adjacency and TOM
`threshold` | Maximum difference allowed between running mean boostrap correlation matrix, and new resampled cor matrix. Defines how soon we consider the bootstrap to have converged.
`tmpSaveFile` | Should temporary saves be done?

### Value

Returns a list of the bootstrapped matrix, standard deviation matrix, and the number of resamplings done.

### Author(s)

David L Gibbs

### Examples

    data(ProCoNA_Data)
    x <- peptideData[,1:10]
    y <- corBootstrap(dataMatrix=x, networkType="unsigned", threshold=0.1, tmpSaveFile=FALSE)

[Package ProCoNA version 1.18.0 Index]