# PB.CI: Provide '(1-alpha)%' confidence interval of each coefficients In EAlasso: Simulation Based Inference of Lasso Estimator

## Description

Using samples drawn by `PBsampler`, computes `(1-alpha)%` confidence interval of each coefficient.

## Usage

 ```1 2``` ```PB.CI(object, alpha = 0.05, method = "debias", parallel = FALSE, ncores = 2L) ```

## Arguments

 `object` bootstrap samples of class `PB` from `PBsampler` `alpha` significance level. `method` bias-correction method. Either to be "none" or "debias". `parallel` logical. If `TRUE`, use parallelization. Default is `FALSE`. `ncores` integer. The number of cores to use for parallelization.

## Details

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.

## Value

`(1-alpha)%` confidence interval of each coefficients

## References

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

## Examples

 ``` 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") ```

EAlasso documentation built on Sept. 1, 2017, 9:03 a.m.