bootMbpca {mogsa} | R Documentation

## Bootstrap mbpca to estimate the coherence of different data sets

### Description

Bootstrap mbpca to estimate the coherence of different data sets and estimate the number of components should be included in an analysis.

### Usage

    bootMbpca(moa, mc.cores = 1, B = 100, replace = TRUE,
      resample = c("sample", "gene", "total"), log = "y", ncomp = NULL, method = NULL,
      maxiter = 1000, svd.solver = c("svd", "fast.svd", "propack"), plot = TRUE)

### Arguments

`moa` | An object of `moa` returned by `mbpca`.
`mc.cores` | Integer; number of cores used in bootstrap. This value is passed to function `mclapply`
`B` | Integer; number of bootstrap
`replace` | Logical; sampling with or without replacement
`resample` | Could be one of "sample", "gene" or "total". "sample" and "gene" means sample-wise and variable-wise resampling, repectively. "total" means total resampling.
`log` | Could be "x", "y" or "xy" for plot log axis
`ncomp` | Passed to function `mbpca`. In most of cases, user do not need to specify this argument because it could be inferred from `moa`.
`method` | Passed to function `mbpca`.In most of cases, user do not need to specify this argument because it could be inferred from `moa`.
`maxiter` | Passed to function `mbpca`.In most of cases, user do not need to specify this argument because it could be inferred from `moa`.
`svd.solver` | Passed to function `mbpca`.In most of cases, user do not need to specify this argument because it could be inferred from `moa`.
`plot` | Logical; whether the result should be plotted.

### Details

Bootstrap method were used to determine the components that are presenting significant concordant structure between datasets.

### Value

It returns a matrix, columns are eigenvalues for different components. Each rows is a bootstramp sample.

### Author(s)

Chen Meng

### Examples

    # see examples in \code{\link{mbpca}}

[Package mogsa version 1.21.0 Index]