idm (version 1.3.1)

update.i_mca: Updates a Multiple Correspondence Analysis solution

Description

This function updates the Multiple Correspondence Analysis (MCA) solution on the indicator matrix using the incremental method of Ross et al. (2008)

Usage

"update"(object, incdata, current_rank, ff = 0, nchunk = 1, ...)

Arguments

object

object of class 'i_mca'

incdata

Matrix of incoming data

current_rank

Rank of approximation; if empty then full rank is used

ff

Number between 0 and 1 indicating the "forgetting factor"

nchunk

Number of blocks processed up to that point; default is 1

...

Further arguments passed to update

Value

indmat

Indicator matrix

colcoord

Column standard coordinates

rowcoord

Row standard coordinates

colpcoord

Column principal coordinates

rowpcoord

Row principal coordinates

colctr

Column contributions

colcor

Column squared correlations

rowctr

Row contributions

rowcor

Row squared correlations

sv

Singular values

rowmass

Row masses

colmass

Column masses

levelnames

Attribute names

m

Number of cases processed up to this point

inertia_e

Percentages of explained (adjusted) inertia

References

Ross, D. A., Lim, J., Lin, R. S., & Yang, M. H. (2008). Incremental learning for robust visual tracking. International Journal of Computer Vision, 77(1-3), 125-141. 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

add_es, i_mca, plot.i_mca

Examples

data(women, package ="idm")
dat = women[,c(1:4)]
res_mca = i_mca(dat[1:300,])
nchunk = seq(301,2107,258)
for (k in c(1:(length(nchunk)-1)))
{
  res_mca = update(res_mca,dat[c((nchunk[k]+1):nchunk[k+1]),],nchunk=k+1)
}
plot(res_mca, what=c(FALSE, TRUE), animation = FALSE)