covSIR | R Documentation |
Sliced Inverse Regression (SIR) can be seen as special case of Supervised ICS (SICS) and this function gives the supervised scatter matrix for SIR
covSIR(X, y, h = 10, ...)
X |
a numeric data matrix. |
y |
a numeric response vector. |
h |
the number of slices. |
... |
arguments passed on to |
This supervised scatter matrix is usually used as the second scatter matrix in SICS to obtain a SIR type supervised linear dimension reduction.
For that purpose covSIR
first divides the response y
into h
slices using the corresponding quantiles as cut points.
Then for each slice the mean vector of X
is computed and the resulting supervised scatter matrix consist of the covariance matrix of these mean vectors.
The function might have problems if the sample size is too small.
a supervised scatter matrix
Klaus Nordhausen
Liski, E., Nordhausen, K. and Oja, H. (2014), Supervised invariant coordinate selection, Statistics: A Journal of Theoretical and Applied Statistics, 48, 711–731. <doi:10.1080/02331888.2013.800067>.
Nordhausen, K., Oja, H. and Tyler, D.E. (2022), Asymptotic and Bootstrap Tests for Subspace Dimension, Journal of Multivariate Analysis, 188, 104830. <doi:10.1016/j.jmva.2021.104830>.
ics
X <- matrix(rnorm(1000), ncol = 5) eps <- rnorm(200, sd = 0.1) y <- 2 + 0.5 * X[, 1] + 2 * X[, 3] + eps covSIR(X, y)
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