bootMoa: Significant components in "moa" returned by function "moa".
In mogsa: Multiple omics data integrative clustering and gene set analysis

Description Usage Arguments Details Value Author(s) References See Also Examples

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

1
2
3

  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)

`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.

set plot=TRUE to help selecting significant components.

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

Chen Meng

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

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