SIR: Sliced Inverse Regression

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

Description

Sliced inverse regression (SIR) is a method used to compute the effective dimension reduction subspace by finding a set of sufficient predictors that contain all of the information in the model matrix X about the outcome Y (Li, 1991). The term inverse regression refers to the fact that the inverse regressions of E[X | Y] is calculated, and the term sliced refers to the fact that Y is divided into smaller non-overlapping intervals (in a manner similar to the loess algorithm).

Usage

SIR(
  formula,
  data,
  rank = "all",
  slices = 5,
  ytype = c("numeric", "categorical")
)

Arguments

formula	a model formula
data	a data frame
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. doi: 10.1080/01621459.1991.10475035

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