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 4 5 6 7 8 9 10 11 12 13 14 
moa 
An object of 
proc.row 
Preprocessing of rows of datasets, should be one of

w.data 
The weights of each separate dataset, should be one of
or 
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 
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 samplewise and variablewise 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 BennaniDosse. STATIS and DISTATIS: optimum multitable principal component analysis and three way metric multidimensional scaling. WIREs Comput Stat 2012. Volume 4, Issue 2, pages 124167 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 moaclass
.
1  # see function moa

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.