View source: R/selectComponents.R
selectComponents | R Documentation |
Takes an array of observations as an input and outputs a subset of the components having the most extreme kurtoses.
selectComponents(x, first = 2, last = 2)
x |
Numeric array of an order at least two. It is assumed that the last dimension corresponds to the sampling units. |
first |
Number of components with maximal kurtosis to be selected. Can equal zero but the total number of components selected must be at least two. |
last |
Number of components with minimal kurtosis to be selected. Can equal zero but the total number of components selected must be at least two. |
In independent component analysis (ICA) the components having the most extreme kurtoses are often thought to be also the most informative. With this viewpoint in mind the function selectComponents
selects from x
first
components having the highest kurtosis and last
components having the lowest kurtoses and outputs them as a standard data matrix for further analysis.
Data matrix with rows corresponding to the observations and the columns correponding to the first
+ last
selected components in decreasing order with respect to kurtosis. The names of the components in the output matrix correspond to the indices of the components in the original array x
.
Joni Virta
data(zip.train)
x <- zip.train
rows <- which(x[, 1] == 0 | x[, 1] == 1)
x0 <- x[rows, 2:257]
x0 <- t(x0)
dim(x0) <- c(16, 16, 2199)
tfobi <- tFOBI(x0)
comp <- selectComponents(tfobi$S)
head(comp)
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