LSIR: Localized Sliced Inverse Regression

In abnormally-distributed/cvreg: Cross Validation and Robust Estimation Utilities

Description

Localized sliced inverse regression (LSIR) is a variant of SIR (Li, 1991) that is capable of sufficient dimension reduction on both linear and nonlinear subspaces. LSIR also avoids the issue of degeneracy in SIR by taking into account of the local structure of the input variables (Wu, Liang, & Mukherjee, 2010).

Usage

LSIR(
  formula,
  data,
  k = 6,
  rank = "all",
  slices = 5,
  ytype = c("numeric", "categorical")
)

Arguments

formula	a model formula
data	a data frame
k	the local neighbordhood size
rank	the desired number of sufficient predictors to return. the default is "all".
slices	the number of slices into which the response variable should be split. defaults to 5. for categorical response variables the maximum allowed is the number of response levels minus one. if set above this, it is silently adjusted.
ytype	either numeric or categorical

Value

an sdr object

References

Li, K-C. (1991). Sliced inverse regression for dimension reduction. Journal of the American Statistical Association, 86(414):316-327

Wu, Q., Liang, F., & Mukherjee, S. (2010). Localized Sliced Inverse Regression. Journal of Computational and Graphical Statistics, 19(4), 843-860.

abnormally-distributed/cvreg documentation built on May 3, 2020, 3:45 p.m.