SIMR: Sliced Inverse Median 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 encapsulate all of the information in the model matrix X about the outcome Y (Li, 1991). Sliced inverse median regression (SIMR) is a simple modification to SIR that uses the conditional medians instead.

Usage

SIMR(
  formula,
  data,
  rank = "all",
  slices = 5,
  med = c("med", "spa"),
  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

Christou, E. (2018). Robust dimension reduction using sliced inverse median regression. Statistical Papers. doi:10.1007/s00362-018-1007-z

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