PcaGrid {rrcov}R Documentation

Robust Principal Components based on Projection Pursuit (PP): GRID search Algorithm

Description

Computes an approximation of the PP-estimators for PCA using the grid search algorithm in the plane.

Usage

    PcaGrid(x, ...)
    ## Default S3 method:
    PcaGrid(x, k = 0, kmax = ncol(x), na.action = na.fail, trace=FALSE, ...)
    ## S3 method for class 'formula':
    PcaGrid(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 NAs. 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 Σ_{j=1}^k l_j/Σ_{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=0.
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.
trace whether to print intermediate results. Default is trace = FALSE

Details

PcaGrid, serving as a constructor for objects of class PcaGrid-class is a generic function with "formula" and "default" methods. For details see PCAgrid and the relevant references.

Value

An S4 object of class PcaGrid-class which is a subclass of the virtual class PcaRobust-class.

Author(s)

Valentin Todorov valentin.todorov@chello.at

References

C. Croux, P. Filzmoser, M. Oliveira, (2007). Algorithms for Projection-Pursuit Robust Principal Component Analysis, Chemometrics and Intelligent Laboratory Systems, 87, 225.

Examples

    # multivariate data with outliers
    library(mvtnorm)
    x <- rbind(rmvnorm(200, rep(0, 6), diag(c(5, rep(1,5)))),
                rmvnorm( 15, c(0, rep(20, 5)), diag(rep(1, 6))))
    # Here we calculate the principal components with PCAgrid
    pc <- PcaGrid(x, 6)
    # we could draw a biplot too:
    biplot(pc)
    
    # we could use another objective function, and 
    # maybe only calculate the first three principal components:
    pc <- PcaGrid(x, 3, method="qn")
    biplot(pc)
    
    # now we want to compare the results with the non-robust principal components
    pc <- PcaClassic(x)
    # again, a biplot for comparision:
    biplot(pc)

[Package rrcov version 0.5-01 Index]