Description Usage Arguments Value
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.
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
)
|
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 |
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 |
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