PcaGrid | R Documentation |
Computes an approximation of the PP-estimators for PCA using the grid search algorithm in the plane.
PcaGrid(x, ...)
## Default S3 method:
PcaGrid(x, k = 0, kmax = ncol(x),
scale=FALSE, na.action = na.fail, crit.pca.distances = 0.975, trace=FALSE, ...)
## S3 method for class 'formula'
PcaGrid(formula, data = NULL, subset, na.action, ...)
formula |
a formula with no response variable, referring only to numeric variables. |
data |
an optional data frame (or similar: see
|
subset |
an optional vector used to select rows (observations) of the
data matrix |
na.action |
a function which indicates what should happen
when the data contain |
... |
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 |
kmax |
maximal number of principal components to compute.
Default is |
scale |
a value indicating whether and how the variables should be
scaled. If |
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 |
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.
An S4 object of class PcaGrid-class
which is a subclass of the
virtual class PcaRobust-class
.
Valentin Todorov valentin.todorov@chello.at
C. Croux, P. Filzmoser, M. Oliveira, (2007). Algorithms for Projection-Pursuit Robust Principal Component Analysis, Chemometrics and Intelligent Laboratory Systems, 87, 225.
Todorov V & Filzmoser P (2009), An Object Oriented Framework for Robust Multivariate Analysis. Journal of Statistical Software, 32(3), 1–47. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v032.i03")}.
# 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, k=3)
# again, a biplot for comparision:
biplot(pc)
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