plot.pcaRes: Plot diagnostics (screeplot)
In pcaMethods: A collection of PCA methods

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

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.

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## S3 method for class 'pcaRes'
plot(x, y = NULL, main = deparse(substitute(object)),
  col = gray(c(0.9, 0.5)), ...)

`x`	`pcaRes` The pcaRes object.
`y`	not used
`main`	title of the plot
`col`	Colors of the bars
`...`	further arguments to barplot

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.

None, used for side effect.

Henning Redestig

screeplot

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data(metaboliteData)
pc <- pca(t(metaboliteData), nPcs=5, cv="q2", scale="uv")
plot(pc)

Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

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    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
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    setdiff, sort, table, tapply, union, unique, unsplit, which,
    which.max, which.min

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Attaching package: 'pcaMethods'

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