nb: Nonparametric Bootstrap
In jeksterslabds/jeksterslabRboot: Jekster's Lab Linear Bootstrap Utilities

Description Usage Arguments Details Value Author(s) References See Also Examples

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
)

`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`.

For more details and examples see the following vignettes:

Notes: Intro to NB

Notes: Intro to PB

Returns a list of length B of nonparametric bootstrap samples.

Ivan Jacob Agaloos Pesigan

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

Wikipedia: Bootstrapping (statistics)

Other bootstrap functions: pbmvn(), pbuniv()

B <- 5L
n <- 5
# vector----------------------------------------------------------------------
x <- rnorm(n = n)
xstar <- nb(
  data = x,
  B = B
)
str(xstar)
# 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)
# data frame------------------------------------------------------------------
X <- as.data.frame(X)
Xstar <- nb(
  data = X,
  B = B
)
str(Xstar)