Rdimtools (version 1.1.2)

do.save: Sliced Average Variance Estimation

Description

Sliced Average Variance Estimation (SAVE) is a supervised linear dimension reduction method. It is based on sufficiency principle with respect to central subspace concept under the linerity and constant covariance conditions. For more details, see the reference paper.

Usage

do.save(
  X,
  response,
  ndim = 2,
  h = max(2, round(nrow(X)/5)),
  preprocess = c("center", "scale", "cscale", "decorrelate", "whiten")
)

Value

a named list containing

Y

an \((n\times ndim)\) matrix whose rows are embedded observations.

trfinfo

a list containing information for out-of-sample prediction.

projection

a \((p\times ndim)\) whose columns are basis for projection.

Arguments

X

an \((n\times p)\) matrix or data frame whose rows are observations and columns represent independent variables.

response

a length-\(n\) vector of response variable.

ndim

an integer-valued target dimension.

h

the number of slices to divide the range of response vector.

preprocess

an additional option for preprocessing the data. Default is "center". See also aux.preprocess for more details.

Author

Kisung You

References

denniscook_method_2000Rdimtools

See Also

do.sir

Examples

## generate swiss roll with auxiliary dimensions
## it follows reference example from LSIR paper.
set.seed(100)
n = 50
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.save(X, y, h=2)
out2 = do.save(X, y, h=5)
out3 = do.save(X, y, h=10)

## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, main="SAVE::2 slices")
plot(out2$Y, main="SAVE::5 slices")
plot(out3$Y, main="SAVE::10 slices")
par(opar)