wefa {ImpuR} | R Documentation |
Compute a progressive principal component analysis (or window evolving factor analysis) for a data matrix.
wefa(data, margin = 5, hetero.correct = 4:8)
data |
a matrix |
margin |
margin arount current row to form the current window |
hetero.correct |
if TRUE , correct principal components by average
of the components selected by hetero.correct
(heteroscedasticity correction) |
the matrix is iteratively analyzed over a sliding window of size 2*margin+1 rows. At each time point, a principal component is performed over the "slice" and the principal components are retained. By default, an average of the principal components from 4 to 8 is subtracted from the others. This takes away the effect of heteroscedasticity when there are only one or two real components and maybe one or two artefacts.
a matrix containing the principal components as columns
Christian Ritter
Ritter and Gilliard (2007), JSS