Sliced Inverse Regression (SIR) is a supervised linear dimension reduction technique. Unlike engineering-driven methods, SIR takes a concept of central subspace, where conditional independence after projection is guaranteed. It first divides the range of response variable. Projection vectors are extracted where projected data best explains response variable.
do.sir(
X,
response,
ndim = 2,
h = max(2, round(nrow(X)/5)),
preprocess = c("center", "scale", "cscale", "decorrelate", "whiten")
)
an
a length-
an integer-valued target dimension.
the number of slices to divide the range of response vector.
an additional option for preprocessing the data.
Default is "center". See also aux.preprocess
for more details.
a named list containing
an
a list containing information for out-of-sample prediction.
a
li_sliced_1991Rdimtools
# NOT RUN {
## generate swiss roll with auxiliary dimensions
## it follows reference example from LSIR paper.
n = 123
theta = runif(n)
h = runif(n)
t = (1+2*theta)*(3*pi/2)
X = array(0,c(n,10))
X[,1] = t*cos(t)
X[,2] = 21*h
X[,3] = t*sin(t)
X[,4:10] = matrix(runif(7*n), nrow=n)
## corresponding response vector
y = sin(5*pi*theta)+(runif(n)*sqrt(0.1))
## try with different numbers of slices
out1 = do.sir(X, y, h=2)
out2 = do.sir(X, y, h=5)
out3 = do.sir(X, y, h=10)
## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, main="SIR::2 slices")
plot(out2$Y, main="SIR::5 slices")
plot(out3$Y, main="SIR::10 slices")
par(opar)
# }
# NOT RUN {
# }
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