fieldboot {boodd} | R Documentation |
Block Bootstrap of Random Field
Description
Performs a bootstrap analysis of multidimensional array representing a random field on a lattice, using various block bootstrap methods such as moving block, circular block, or nonoverlapping block bootstrap.
Usage
fieldboot(
arr,
func,
B,
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 |
func |
The function applied to each bootstrap sample. |
B |
A positive integer; the number of bootstrap samples. |
length.block |
A positive 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. |
... |
Optional additional arguments for the |
method |
The method for array reconstruction:
|
Details
The fieldboot
function resamples hyper-rectangles constructed using either
moving blocks, nonoverlapping blocks or circular blocks to construct a bootstrap field of the same
dimension as the original one. Then it
applies the specified func
to bootstrap samples of the
provided data array. The length.block
parameter determines the size of the blocks
used in the bootstrap method. The method
parameter specifies the type of block
bootstrap to use. This function is useful for assessing the variability and distribution
properties of a statistic in the context of random fields.
Value
Returns an object of class boodd
.
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
, fieldbootP
.
Examples
set.seed(123)
arr <- array(rnorm(1000), dim = c(10, 10, 10))
res <- fieldboot(arr, mean, B = 100, length.block = c(2, 2, 2))
plot(res)