R: Principal component analysis for real data

princomp.rmult {compositions} | R Documentation

## Principal component analysis for real data

### Description

Performs a principal component analysis for datasets of type rmult.

### Usage

    ## S3 method for class 'rmult'
    princomp(x,cor=FALSE,scores=TRUE,
                       covmat=var(rmult(x[subset,]),robust=robust,giveCenter=TRUE),
                       center=attr(covmat,"center"),  subset = rep(TRUE, nrow(x)),
                       ..., robust=getOption("robust"))

### Arguments

`x` | a rmult-dataset
`...` | Further arguments to call `princomp.default`
`cor` | logical: shall the computation be based on correlations rather than covariances?
`scores` | logical: shall scores be computed?
`covmat` | provides the covariance matrix to be used for the principle component analysis
`center` | provides the be used for the computation of scores
`subset` | A rowindex to x giving the columns that should be used to estimate the variance.
`robust` | Gives the robustness type for the calculation of the covariance matrix. See `var.rmult` for details.

### Details

The function just does `princomp(unclass(x),...,scale=scale)` and is only here for convenience.

### Value

An object of type `princomp` with the following fields

`sdev` | the standard deviation of the principal components.
`loadings` | the matrix of variable loadings (i.e., a matrix whose columns contain the eigenvectors). This is of class `"loadings"`.
`center` | the mean that was substracted from the data set
`scale` | the scaling applied to each variable
`n.obs` | number of observations
`scores` | if `scores = TRUE`, the scores of the supplied data on the principal components. Scores are coordinates in a basis given by the principal components.
`call` | the matched call
`na.action` | Not clearly understood

### Author(s)

K.Gerald v.d. Boogaart http://www.stat.boogaart.de

### See Also

`princomp.rplus`

### Examples

    data(SimulatedAmounts)
    pc <- princomp(rmult(sa.lognormals5))
    pc
    summary(pc)
    plot(pc)
    screeplot(pc)
    screeplot(pc,type="l")
    biplot(pc)
    biplot(pc,choice=c(1,3))
    loadings(pc)
    plot(loadings(pc))
    pc$sdev^2
    cov(predict(pc,sa.lognormals5))

[Package compositions version 1.40-3 Index]