Description Usage Arguments Details Value References Examples
Groupwise SIR (gSIR) for binary response
1 | gSIR(X, Y, groups, dims)
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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. |
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.
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.
Guo, Z., Li, L., Lu, W., and Li, B. (2014). Groupwise dimension reduction via envelope method. Journal of the American Statistical Association, accepted.
1 2 3 4 5 6 7 | 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)
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