Description Usage Arguments Details Value References Examples
Using samples drawn by PBsampler
, computes
(1-alpha)%
confidence interval of each coefficient.
1 2 |
object |
bootstrap samples of class |
alpha |
significance level. |
method |
bias-correction method. Either to be "none" or "debias". |
parallel |
logical. If |
ncores |
integer. The number of cores to use for parallelization. |
If method==none
, PB.CI
simply compute
the two-soded (1-alpha)
quantile of the sampled coefficients.
If method==debias
, we use
debiased estimator to compute confidence interval.
(1-alpha)%
confidence interval of each coefficients
Zhang, C., Zhang, S. (2014), "Confidence intervals for low dimensional parameters in high dimensional linear models," Journal of the Royal Statistical Society: Series B, 76, 217<e2><80><93>242.
Dezeure, R., Buehlmann, P., Meier, L. and Meinshausen, N. (2015), "High-Dimensional Inference: Confidence Intervals, p-values and R-Software hdi," Statistical Science, 30(4), 533-558
1 2 3 4 5 6 7 8 9 10 11 | set.seed(1234)
n <- 40
p <- 50
Niter <- 10
Group <- rep(1:(p/10), each = 10)
Weights <- rep(1, p/10)
X <- matrix(rnorm(n*p), n)
object <- PBsampler(X = X, PE_1 = c(1,1,rep(0,p-2)), sig2_1 = 1, lbd_1 = .5,
niter = 100, type = "lasso")
parallel <- (.Platform$OS.type != "windows")
PB.CI(object = object, alpha = .05, method = "none")
|
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