fieldbootP {boodd} | R Documentation |
Bootstrap Sample from the Random Field
Description
Function returns a bootstrap sample of the random field on a lattice, using various block bootstrap methods such as moving block, circular block, or nonoverlapping block bootstrap.
Usage
fieldbootP(
arr,
length.block,
method = c("movingblock", "nonoverlapping", "circular")
)
Arguments
arr |
A multidimensional real-valued array; it represents a random field on a grid of dimension
equals to dimension of the |
length.block |
An integer or vector of integers; it specified the block lengths for blocks. If a scalar is provided, the same block length is used for all dimensions. |
method |
The method for array reconstruction:
|
Details
In the case of random fields, the blocks B_{i}
's are random matrices of size
length.block
. From the set of all blocks
\mathcal{B}=\{B_{1},B_{2},\dots,B_{q}\},
we select
randomly with replacement blocks B_{1}^{\ast},\dots,B_{l}^{\ast}
and bind them together. The
probability of choosing any block is 1/q
.
Value
Returns a bootstrap sample of given arr
.
References
Bertail, P. and Dudek, A. (2025). Bootstrap for Dependent Data, with an R package (by Bernard Desgraupes and Karolina Marek) - submitted.
Bertail, P. Politis, D. N. Rhomari, N. (2000). Subsampling continuous parameter random fields and a Bernstein inequality, Statistics, 33, 367-392.
Nordman, D.J. Lahiri, S.N.(2004). On optimal spatial subsample size for variance estimation, The Annals of Statistics, 32, 1981-2027.
Politis, D.N. Romano, J.P. (1993). Nonparametric Resampling for Homogeneous Strong Mixing Random Fields, J. Multivar. Anal., 47, 301-328.
See Also
blockboot
,
jackVarField
,
field.sub
, fieldboot
.
Examples
library(geoR)
N=10
n=N^2
sim <- grf(n, grid="reg", cov.pars=c(1, .25), nsim=1)
image(sim)
arr=array(sim$data,dim=c(N,N))
mb=fieldbootP(arr,length.block=c(2,2),method="movingblock")
simb=sim
simb$data=as.vector(mb)
image(simb)