# BSSplitLasso: Bootstrap-calibrated R-split method In WaverlyWei/debiased.subgroup: Sharp Inference on Selected Subgroup in Observational Studies

## Description

This method first obtains the estimate of β via repetitive splitting procedure (R-Split) through BB iterations. Then it calculates the calibration term \tilde{b}_{max} = (1-n^{r-0.5})(\tilde{β}_{max}-\tilde{β}_{j}). Through B iterations, it recalibrates the bootstrap statistic T_b. The bias-reduced estimate is computed as: \tilde{b}_{max}-\frac{1}{B}∑_{b=1}^B T_b.

## Usage

  1 2 3 4 5 6 7 8 9 10 11 BSSplitLasso( y, x, r = NULL, G = NULL, B = NULL, BB = NULL, alpha = 0.95, splitRatio = 0.6, fold = 2 ) 

## Arguments

 y response x design matrix r tuning parameter G subgroup indicator B bootstrap number BB split number alpha level ## change other places splitRatio split ratio fold cross validation fold

## Value

 LowerBound lower confidence bound UpperBound upper confidence bound betaMax bias-reduced maximum beta estimate betaEst debiased beta estimate for each subgroup modelSize selected model size for R-split op optimal tuning

WaverlyWei/debiased.subgroup documentation built on Jan. 27, 2021, 12:15 a.m.