idm (version 1.3.1)

update.i_pca: 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

"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

Column names

rowctr

Row contributions

rowcor

Row squared correlations

colctr

Column contributions

colcor

Column squared correlations

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

data(iris, package="datasets")
dat = scale(iris[,-5])
res_pca = i_pca(dat[1:50,-5])
aa = seq(50,150,50)
for (k in c(1:2)) {
  res_pca = update(res_pca, dat[c((aa[k]+1):aa[k+1]),])
} 
plot(res_pca, animation = FALSE)