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R: NIPALS PCA implemented in R

RnipalsPca

R Documentation

NIPALS PCA implemented in R

Description

PCA by non-linear iterative partial least squares

Usage

RnipalsPca(Matrix, nPcs = 2, varLimit = 1, maxSteps = 5000,
  threshold = 1e-06, verbose = interactive(), ...)

Arguments

`Matrix`	Pre-processed (centered, scaled) numerical matrix samples in rows and variables as columns.
`nPcs`	Number of components that should be extracted.
`varLimit`	Optionally the ratio of variance that should be explained. `nPcs` is ignored if varLimit < 1
`maxSteps`	Defines how many iterations can be done before algorithm should abort (happens almost exclusively when there were some wrong in the input data).
`threshold`	The limit condition for judging if the algorithm has converged or not, specifically if a new iteration is done if (T_{old} - T)^T(T_{old} - T) > code{limit}.
`verbose`	Show simple progress information.
`...`	Only used for passing through arguments.

Details

Can be used for computing PCA on a numeric matrix using either the NIPALS algorithm which is an iterative approach for estimating the principal components extracting them one at a time. NIPALS can handle a small amount of missing values. It is not recommended to use this function directely but rather to use the pca() wrapper function. There is a C++ implementation given as nipalsPca which is faster.

Value

A pcaRes object.

Author(s)

Henning Redestig

References

Wold, H. (1966) Estimation of principal components and related models by iterative least squares. In Multivariate Analysis (Ed., P.R. Krishnaiah), Academic Press, NY, 391-420.

Examples

data(metaboliteData)
mat <- prep(t(metaboliteData))
## c++ version is faster
system.time(pc <- RnipalsPca(mat, method="rnipals", nPcs=2))
system.time(pc <- nipalsPca(mat, nPcs=2))
## better use pca()
pc <- pca(t(metaboliteData), method="rnipals", nPcs=2)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(pcaMethods)
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.


Attaching package: 'pcaMethods'

The following object is masked from 'package:stats':

    loadings

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/pcaMethods/RnipalsPca.Rd_%03d_medium.png", width=480, height=480)
> ### Name: RnipalsPca
> ### Title: NIPALS PCA implemented in R
> ### Aliases: RnipalsPca
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(metaboliteData)
> mat <- prep(t(metaboliteData))
> ## c++ version is faster
> system.time(pc <- RnipalsPca(mat, method="rnipals", nPcs=2))
   user  system elapsed 
  0.044   0.000   0.042 
> system.time(pc <- nipalsPca(mat, nPcs=2))
   user  system elapsed 
  0.004   0.000   0.002 
> ## better use pca()
> pc <- pca(t(metaboliteData), method="rnipals", nPcs=2)
> ## Don't show: 
> stopifnot(sum((fitted(pc) - t(metaboliteData))^2, na.rm=TRUE) < 200)
> ## End(Don't show)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>