BSSplitLasso: Bootstrap-calibrated R-split method

Description Usage Arguments Value

View source: R/BSSplitLasso.R

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

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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.