BRISC_bootstrap | R Documentation |
The function BRISC_bootstrap
performs bootstrap to provide confidence intervals for parameters of univariate spatial
regression models using outputs of BRISC_estimation
. The details of the bootstrap method can be found in BRISC
(Saha & Datta, 2018). The optimization is performed with C library of limited-memory BFGS libLBFGS: a library of
Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS), http://www.chokkan.org/software/liblbfgs/ (Naoaki Okazaki).
For user convenience the soure codes of the package libLBFGS are provided in the package. Some code blocks are borrowed
from the R package: spNNGP: Spatial Regression Models for Large Datasets using Nearest Neighbor Gaussian Processes
https://CRAN.R-project.org/package=spNNGP .
BRISC_bootstrap(BRISC_Out, n_boot = 100, h = 1, n_omp = 1, init = "Initial", verbose = TRUE, nugget_status = 1)
BRISC_Out |
an object of class |
n_boot |
number of bootstrap samples. Default value is 100. |
h |
number of core to be used in parallel computing setup for bootstrap samples. If |
n_omp |
number of threads to be used, value can be more than 1 if source code is compiled with OpenMP support. Default is 1. |
init |
keyword that specifies initialization scheme to be used. Supported keywords are: |
verbose |
if |
nugget_status |
if |
A list comprising of the following:
boot.Theta |
estimates of spatial covariance parameters corresponding to bootstrap samples. |
boot.Beta |
estimates of beta corresponding to bootstrap samples. |
confidence.interval |
confidence intervals corresponding to the parameters. |
boot.time |
time (in seconds) required to perform the bootstrapping after preprocessing data in |
Arkajyoti Saha arkajyotisaha93@gmail.com,
Abhirup Datta abhidatta@jhu.edu
Saha, A., & Datta, A. (2018). BRISC: bootstrap for rapid inference on spatial covariances. Stat, e184, DOI: 10.1002/sta4.184.
Okazaki N. libLBFGS: a library of Limited-memory Broyden-Fletcher-Goldfarb-Shanno
(L-BFGS),
http://www.chokkan.org/software/liblbfgs/ .
Andrew Finley, Abhirup Datta and Sudipto Banerjee (2017). spNNGP: Spatial Regression Models for Large Datasets using Nearest Neighbor Gaussian Processes. R package version 0.1.1. https://CRAN.R-project.org/package=spNNGP
rmvn <- function(n, mu = 0, V = matrix(1)){ p <- length(mu) if(any(is.na(match(dim(V),p)))) stop("Dimension not right!") D <- chol(V) t(matrix(rnorm(n*p), ncol=p)%*%D + rep(mu,rep(n,p))) } set.seed(1) n <- 300 coords <- cbind(runif(n,0,1), runif(n,0,1)) beta <- c(1,5) x <- cbind(rnorm(n), rnorm(n)) sigma.sq = 1 phi = 5 tau.sq = 0.1 B <- as.matrix(beta) D <- as.matrix(dist(coords)) R <- exp(-phi*D) w <- rmvn(1, rep(0,n), sigma.sq*R) y <- rnorm(n, x%*%B + w, sqrt(tau.sq)) estimation_result <- BRISC_estimation(coords, y, x) bootstrap_result <- BRISC_bootstrap(estimation_result, n_boot = 10)
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