Nonparametric Bootstrap

Source: R/boot.R

Generates B number of nonparametric bootstrap samples from the original sample data. data is referred to as the empirical distribution \( \hat{ F } %(\#eq:boot-ecdf) \).

nb(
  data,
  B = 2000L,
  par = FALSE,
  ncores = NULL,
  mc = TRUE,
  lb = FALSE,
  cl_eval = FALSE,
  cl_export = FALSE,
  cl_expr,
  cl_vars
)

Arguments

data

Vector, matrix or data frame. Sample data to bootstrap. The empirical distribution \( \hat{ F } %(\#eq:boot-ecdf) \).
B

Integer. Number of bootstrap samples.
par

Logical. If TRUE, use multiple cores. If FALSE, use lapply().
ncores

Integer. Number of cores to use if par = TRUE. If unspecified, defaults to detectCores() - 1.
mc

Logical. If TRUE, use parallel::mclapply(). If FALSE, use parallel::parLapply() or parallel::parLapplyLB(). Ignored if par = FALSE.
lb

Logical. If TRUE use parallel::parLapplyLB(). If FALSE, use parallel::parLapply(). Ignored if par = FALSE and mc = TRUE.
cl_eval

Logical. Execute parallel::clusterEvalQ() using cl_expr. Ignored if mc = TRUE.
cl_export

Logical. Execute parallel::clusterExport() using cl_vars. Ignored if mc = TRUE.
cl_expr

Expression. Expression passed to parallel::clusterEvalQ() Ignored if mc = TRUE.
cl_vars

Character vector. Names of objects to pass to parallel::clusterExport() Ignored if mc = TRUE.

Value

Returns a list of length B of nonparametric bootstrap samples.

Details

For more details and examples see the following vignettes:

Notes: Intro to NB

Notes: Intro to PB

References

Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap. New York, N.Y: Chapman & Hall.

Wikipedia: Bootstrapping (statistics)

See also

Other bootstrap functions: pbmvn(), pbuniv()

Examples

B <- 5L
n <- 5
# vector----------------------------------------------------------------------
x <- rnorm(n = n)
xstar <- nb(
  data = x,
  B = B
)
str(xstar)
#> List of 5
#>  $ : num [1:5] -0.105 -1.569 0.709 0.709 -3.002
#>  $ : num [1:5] -3.002 -1.569 -0.902 -3.002 0.709
#>  $ : num [1:5] -0.105 -0.105 -1.569 -1.569 0.709
#>  $ : num [1:5] -1.569 -3.002 -0.902 -0.902 -0.105
#>  $ : num [1:5] -1.569 0.709 -3.002 0.709 -0.105
# matrix----------------------------------------------------------------------
x1 <- rnorm(n = n)
x2 <- rnorm(n = n)
x3 <- rnorm(n = n)
X <- cbind(x1, x2, x3)
Xstar <- nb(
  data = X,
  B = B
)
str(Xstar)
#> List of 5
#>  $ : num [1:5, 1:3] -0.33 0.938 0.698 0.362 -0.33 ...
#>   ..- attr(*, "dimnames")=List of 2
#>   .. ..$ : chr [1:5] "1" "2" "3" "4" ...
#>   .. ..$ : chr [1:3] "x1" "x2" "x3"
#>  $ : num [1:5, 1:3] -1.371 -0.33 0.698 0.938 0.362 ...
#>   ..- attr(*, "dimnames")=List of 2
#>   .. ..$ : chr [1:5] "1" "2" "3" "4" ...
#>   .. ..$ : chr [1:3] "x1" "x2" "x3"
#>  $ : num [1:5, 1:3] -0.33 0.938 -0.33 0.362 0.362 ...
#>   ..- attr(*, "dimnames")=List of 2
#>   .. ..$ : chr [1:5] "1" "2" "3" "4" ...
#>   .. ..$ : chr [1:3] "x1" "x2" "x3"
#>  $ : num [1:5, 1:3] 0.938 0.938 -0.33 0.698 0.698 ...
#>   ..- attr(*, "dimnames")=List of 2
#>   .. ..$ : chr [1:5] "1" "2" "3" "4" ...
#>   .. ..$ : chr [1:3] "x1" "x2" "x3"
#>  $ : num [1:5, 1:3] 0.938 -1.371 -1.371 -0.33 0.362 ...
#>   ..- attr(*, "dimnames")=List of 2
#>   .. ..$ : chr [1:5] "1" "2" "3" "4" ...
#>   .. ..$ : chr [1:3] "x1" "x2" "x3"
# data frame------------------------------------------------------------------
X <- as.data.frame(X)
Xstar <- nb(
  data = X,
  B = B
)
str(Xstar)
#> List of 5
#>  $ :'data.frame':	5 obs. of  3 variables:
#>   ..$ x1: num [1:5] 0.362 -0.33 0.938 0.938 0.698
#>   ..$ x2: num [1:5] -0.59949 0.7343 -0.00716 -0.00716 -0.71075
#>   ..$ x3: num [1:5] -0.956 -0.705 0.183 0.183 -0.328
#>  $ :'data.frame':	5 obs. of  3 variables:
#>   ..$ x1: num [1:5] -0.33 -1.371 0.362 -1.371 -0.33
#>   ..$ x2: num [1:5] 0.734 -0.347 -0.599 -0.347 0.734
#>   ..$ x3: num [1:5] -0.705 -1.036 -0.956 -1.036 -0.705
#>  $ :'data.frame':	5 obs. of  3 variables:
#>   ..$ x1: num [1:5] -1.371 -0.33 -0.33 -1.371 0.938
#>   ..$ x2: num [1:5] -0.347 0.7343 0.7343 -0.347 -0.00716
#>   ..$ x3: num [1:5] -1.036 -0.705 -0.705 -1.036 0.183
#>  $ :'data.frame':	5 obs. of  3 variables:
#>   ..$ x1: num [1:5] 0.938 0.698 0.698 -0.33 0.938
#>   ..$ x2: num [1:5] -0.00716 -0.71075 -0.71075 0.7343 -0.00716
#>   ..$ x3: num [1:5] 0.183 -0.328 -0.328 -0.705 0.183
#>  $ :'data.frame':	5 obs. of  3 variables:
#>   ..$ x1: num [1:5] -1.371 0.362 -1.371 -0.33 0.938
#>   ..$ x2: num [1:5] -0.347 -0.59949 -0.347 0.7343 -0.00716
#>   ..$ x3: num [1:5] -1.036 -0.956 -1.036 -0.705 0.183