bootMoa {mogsa} | R Documentation

## Significant components in "moa" returned by function "moa".

### Description

Using bootstrap method to extract the components representing significant concordance structures between datasets from "moa" (returned by function "moa").

### Usage

      bootMoa(moa, proc.row="center_ssq1", w.data="inertia", w.row=NULL, statis=FALSE,
        mc.cores=1, B = 100, replace=TRUE, resample=c("sample", "gene", "total"),
        plot=TRUE, log="y", tol = 1e-7)

### Arguments

`moa` | An object of `moa` returned by `moa`.
`proc.row` | Preprocessing of rows of datasets, should be one of `none` - no preprocessing, `center` - center only, `center_ssq1` - center and scale (sum of squred values equals 1), `center_ssqN` - center and scale (sum of squred values equals the number of columns), `center_ssqNm1` - center and scale (sum of squred values equals the number of columns - 1) MFA corresponds to "proc.row=center_ssq1" and 'w.data="lambda1"'
`w.data` | The weights of each separate dataset, should be one of `uniform` - no weighting, `lambda1` - weighted by the reverse of the first eigenvalue of each individual dataset or `inertia` - weighted by the reverse of the total inertia. See detail.
`w.row` | If it is not null, it should be a list of positive numerical vectors, the length of which should be the same with the number of rows of each dataset to indicated the weight of rows of datasets.
`statis` | A logical indicates whether STATIS method should be used. See details.
`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.
`plot` | Logical; whether the result should be plotted.
`log` | Could be "x", "y" or "xy" for plot log axis.
`tol` | The minimum eigenvalues shown in the plot.

### Details

set plot=TRUE to help selecting significant components.

### Value

A matrix where columns are components and rows are variance of PCs from bootstrap samples.

### Author(s)

Chen Meng

### References

Herve Abdi, Lynne J. Williams, Domininique Valentin and Mohammed Bennani-Dosse. STATIS and DISTATIS: optimum multitable principal component analysis and three way metric multidimensional scaling. WIREs Comput Stat 2012. Volume 4, Issue 2, pages 124-167 Herve Abdi, Lynne J. Williams, Domininique Valentin. Multiple factor analysis: principal component analysis for multitable and multiblock data sets. WIREs Comput Stat 2013

### See Also

`moa`, `sup.moa`, `mogsa`. More about plot see `moa-class`.

### Examples

      # see function moa

[Package mogsa version 1.21.0 Index]