bootMoa: Significant components in "moa" returned by function "moa".

In mengchen18/mogsa: Multiple omics data integrative clustering and gene set analysis

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-07
)

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

mengchen18/mogsa documentation built on June 7, 2020, 6:05 p.m.