gSIR.comp.d: Groupwise SIR (gSIR) BIC criterion to estimate dimensions...

In sSDR: Tools Developed for Structured Sufficient Dimension Reduction (sSDR)

Description

Groupwise SIR (gSIR) BIC criterion to estimate dimensions with eigen-decomposition (binary response)

Usage

gSIR.comp.d(X, y, groups)

Arguments

X	A covariate matrix of n observations and p predictors.
y	A binary response.
groups	A vector with the number of predictors in each group.

Details

This function estimates dimension for each predictor group using eigen-decomposition. Predictors need to be organized in groups within the "X" matrix, as the same order saved in "groups". We only allow continuous covariates in the "X" matrix; while categorical covariates can be handled outside of gSIR, e.g. structured SIR.

Value

gSIR.comp.d returns a list containning at least the following components: "d", the estimated dimension (at most 1) for each predictor group; "crit", the BIC criterion from each iteration.

References

Liu, Y. (2015). Approaches to reduce and integrate data in structured and high-dimensional regression problems in Genomics. Ph.D. Dissertation, The Pennsylvania State University, University Park, Department of Statistics.

Examples

data <- gen.data(n=1000, binary=TRUE) # generate data
dim(data$X) # covariate matrix of 1000 observations and 15 predictors
length(data$y) # univariate response
groups <- c(5, 10) # two predictor groups and their numbers of predictors
dim_gSIR<-gSIR.comp.d(data$X,data$y,groups)
names(dim_gSIR)

sSDR documentation built on May 1, 2019, 8:23 p.m.