Updates a Principal Component Analysis solution

Description

This function updates the Principal Component Analysis (PCA) solution on the covariance matrix using the incremental method of Hall, Marshall & Martin (2002)

Usage

1
2
## S3 method for class 'i_pca'
update(object, incdata, ...)

Arguments

object

object of class 'i_pca'

incdata

matrix of incoming data

...

Further arguments passed to update

Value

rowpcoord

Row scores on the principal components

colpcoord

Variable loadings

eg

A list describing the eigenspace of a data matrix, with components
u Left eigenvectors
v Right eigenvectors
m Number of cases
d Eigenvalues
orgn Data mean

inertia.e

Percentages of explained variance

sv

Singular values

levelnames

Variable names

rowcor

Row squared correlations

rowctr

Row contributions

colcor

Column squared correlations

colctr

Column contributions

References

Hall, P., Marshall, D., & Martin, R. (2002). Adding and subtracting eigenspaces with eigenvalue decomposition and singular value decomposition. Image and Vision Computing, 20(13), 1009-1016.

Iodice D' Enza, A., & Markos, A. (2015). Low-dimensional tracking of association structures in categorical data, Statistics and Computing, 25(5), 1009-1022.

See Also

update.i_mca, i_pca, i_mca, add_es

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
data(segmentationData, package = "caret")
HCS = data.frame(scale(segmentationData[,-c(1:3)]))
names(HCS) = abbreviate(names(HCS), minlength = 5)
res_PCA = i_pca(HCS[1:200, ])
aa = seq(from = 201, to = nrow(HCS), by = 200)
aa[length(aa)] = nrow(HCS)
for (k in c(1:(length(aa)-1))){
     res_pca = update(res_PCA, HCS[c((aa[k]):(aa[k+1]-1)),])
    }
#Static plot
plot(res_PCA, animation = FALSE)