Plot the computed diagnostics of PCA model to get an idea of their
importance. Note though that the standard screeplot shows the
standard deviations for the PCs this method shows the R2 values
which empirically shows the importance of the P's and is thus
applicable for any PCA method rather than just SVD based PCA.
Usage
## S3 method for class 'pcaRes'
plot(x, y = NULL, main = deparse(substitute(object)),
col = gray(c(0.9, 0.5)), ...)
Arguments
x
pcaRes The pcaRes object.
y
not used
main
title of the plot
col
Colors of the bars
...
further arguments to barplot
Details
If cross-validation was done for the PCA the plot will also show
the CV based statistics. A common rule-of-thumb for determining
the optimal number of PCs is the PC where the CV diagnostic is at
its maximum but not very far from R^2.
Value
None, used for side effect.
Author(s)
Henning Redestig
See Also
screeplot
Examples
data(metaboliteData)
pc <- pca(t(metaboliteData), nPcs=5, cv="q2", scale="uv")
plot(pc)
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)
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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/plot.pcaRes.Rd_%03d_medium.png", width=480, height=480)
> ### Name: plot.pcaRes
> ### Title: Plot diagnostics (screeplot)
> ### Aliases: plot,pcaRes-method plot.pcaRes
>
> ### ** Examples
>
> data(metaboliteData)
> pc <- pca(t(metaboliteData), nPcs=5, cv="q2", scale="uv")
> plot(pc)
>
>
>
>
>
> dev.off()
null device
1
>