biplot-methods: Plot a overlaid scores and loadings plot
In pcaMethods: A collection of PCA methods

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

Visualize two-components simultaneously

## S3 method for class 'pcaRes'
biplot(x, choices = 1:2, scale = 1,
  pc.biplot = FALSE, ...)

## S4 method for signature 'pcaRes'
biplot(x, choices = 1:2, scale = 1,
  pc.biplot = FALSE, ...)

`x`	a pcaRes object
`choices`	which two pcs to plot
`scale`	The variables are scaled by lambda^scale and the observations are scaled by lambda ^ (1-scale) where `lambda` are the singular values as computed by `princomp`. Normally 0 <= scale <= 1, and a warning will be issued if the specified 'scale' is outside this range.
`pc.biplot`	If true, use what Gabriel (1971) refers to as a "principal component biplot", with lambda = 1 and observations scaled up by sqrt(n) and variables scaled down by sqrt(n). Then the inner products between variables approximate covariances and distances between observations approximate Mahalanobis distance.
`...`	optional arguments to be passed to `biplot.default`.

This is a method for the generic function 'biplot'. There is considerable confusion over the precise definitions: those of the original paper, Gabriel (1971), are followed here. Gabriel and Odoroff (1990) use the same definitions, but their plots actually correspond to pc.biplot = TRUE.

a plot is produced on the current graphics device.

Kevin Wright, Adapted from biplot.prcomp

prcomp, pca, princomp

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data(iris)
pcIr <- pca(iris[,1:4])
biplot(pcIr)

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, sd, var, xtabs

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

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colMeans, colSums, 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, rowMeans, rowSums, rownames, sapply,
    setdiff, sort, table, tapply, union, unique, unsplit, which,
    which.max, which.min

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Attaching package: 'pcaMethods'

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

    loadings