| gSIR {sSDR} | R Documentation |
Groupwise SIR (gSIR) for binary response
Description
Groupwise SIR (gSIR) for binary response
Usage
gSIR(X, Y, groups, dims)
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. |
dims |
A vector with the dimension (at most 1) for each predictor group. |
Details
This function estimates directions for each predictor group using gSIR. 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 returns a list containning at least the following components: "b_est", the estimated directions for each group with its own dimension using gSIR AFTER normalization; "B", the estimated directions for each group using gSIR BEFORE normalization.
References
Guo, Z., Li, L., Lu, W., and Li, B. (2014). Groupwise dimension reduction via envelope method. Journal of the American Statistical Association, accepted.
Examples
data <- gen.data(n=1000, binary=TRUE) # generate data
dim(data$X) # covariate matrix of 1000 observations and 15 predictors
length(data$y) # binary response
groups <- c(5, 10) # two predictor groups and their numbers of predictors
dims <- c(1,1) # dimension of each predictor group
est_gSIR<-gSIR(data$X,data$y,groups,dims)
names(est_gSIR)