R: Principal Components Analysis

PcaClassic {rrcov}

R Documentation

Principal Components Analysis

Description

Performs a principal components analysis and returns the results as an object of class PcaClassic (aka constructor).

Usage

PcaClassic(x, ...)
## Default S3 method:
PcaClassic(x, k = ncol(x), kmax = ncol(x), 
    scale=FALSE, signflip=TRUE, crit.pca.distances = 0.975, trace=FALSE, ...)
## S3 method for class 'formula'
PcaClassic(formula, data = NULL, subset, na.action, ...)

Arguments

`formula`	a formula with no response variable, referring only to numeric variables.
`data`	an optional data frame (or similar: see `model.frame`) containing the variables in the formula `formula`.
`subset`	an optional vector used to select rows (observations) of the data matrix `x`.
`na.action`	a function which indicates what should happen when the data contain `NA`s. The default is set by the `na.action` setting of `options`, and is `na.fail` if that is unset. The default is `na.omit`.
`...`	arguments passed to or from other methods.
`x`	a numeric matrix (or data frame) which provides the data for the principal components analysis.
`k`	number of principal components to compute. If `k` is missing, or `k = 0`, the algorithm itself will determine the number of components by finding such `k` that `l_k/l_1 >= 10.E-3` and `\Sigma_{j=1}^k l_j/\Sigma_{j=1}^r l_j >= 0.8`. It is preferable to investigate the scree plot in order to choose the number of components and then run again. Default is `k=ncol(x)`.
`kmax`	maximal number of principal components to compute. Default is `kmax=10`. If `k` is provided, `kmax` does not need to be specified, unless `k` is larger than 10.
`scale`	a value indicating whether and how the variables should be scaled to have unit variance (only possible if there are no constant variables). If `scale=FALSE` (default) or `scale=NULL` no scaling is performed (a vector of 1s is returned in the scale slot). If `scale=TRUE` the data are scaled to have unit variance. Alternatively it can be a function like `sd` or `Qn` or a vector of length equal the number of columns of `x`. The value is passed to the underlying function and the result returned is stored in the scale slot. Default is `scale=FALSE`.
`signflip`	a logical value indicating wheather to try to solve the sign indeterminancy of the loadings - ad hoc approach setting the maximum element in a singular vector to be positive. Default is `signflip = TRUE`
`crit.pca.distances`	criterion to use for computing the cutoff values for the orthogonal and score distances. Default is 0.975.
`trace`	whether to print intermediate results. Default is `trace = FALSE`

Value

An S4 object of class PcaClassic-class which is a subclass of the virtual class Pca-class.

Note

This function can be seen as a wrapper arround prcomp() from stats which returns the results of the PCA in a class compatible with the object model for robust PCA.

Author(s)

Valentin Todorov valentin.todorov@chello.at

References

Todorov V & Filzmoser P (2009), An Object Oriented Framework for Robust Multivariate Analysis. Journal of Statistical Software, 32(3), 1–47. doi:10.18637/jss.v032.i03.