Description Usage Arguments Details Value Examples
Provides confidence intervals for the set of active coefficients from lasso estimator using Metropolis-Hastings sampler.
1 2 3 |
X |
predictor matrix. |
Y |
response vector. |
lbd |
penalty term of lasso. By letting this argument be |
weights |
weight vector with length equal to the number of coefficients.
Default is |
tau |
numeric vector. Standard deviaion of proposal distribution
for each beta. Adjust the value to get relevant level of acceptance rate.
Default is |
sig2.hat |
variance of error term. |
alpha |
confidence level for confidence interval. |
nChain |
the number of chains. For each chain, different plug-in beta will be generated from its confidence region. |
niterPerChain |
the number of iterations per chain. |
parallel |
logical. If |
ncores |
integer. The number of cores to use for parallelization. |
returnSamples |
logical. If |
... |
auxiliary |
This function provides post-selection inference for lasso estimator.
Using Metropolis-Hastings sampler with multiple chains, generates (1-alpha)
confidence interval for each active coefficients.
Set returnSamples = TRUE
to check the samples.
Check the acceptance rate and adjust tau
accordingly.
We recommend to set nChain >= 10
and niterPerChain >= 500
.
MHsamples |
a list of class MHLS. |
confidenceInterval |
(1-alpha) confidence interval for each active coefficient. |
1 2 3 4 5 6 7 8 9 10 11 12 | set.seed(123)
n <- 5
p <- 10
X <- matrix(rnorm(n*p),n)
Y <- X %*% rep(1,p) + rnorm(n)
sig2 <- 1
lbd <- .37
weights <- rep(1,p)
Postinference.MHLS(X = X, Y = Y, lbd = lbd, sig2.hat = 1, alpha = .05,
nChain = 3, niterPerChain = 20, parallel = TRUE)
Postinference.MHLS(X = X, Y = Y, lbd = lbd, sig2.hat = 1, alpha = .05,
nChain = 3, niterPerChain = 20, parallel = TRUE, returnSamples = TRUE)
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