Jackknife

Generates jackknife samples.

jack(
  data,
  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.
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 jackknife samples of length n, where n is the sample size of data. Each item in the list, will have a sample size of n - 1.

Details

$$ \mathbf{ x }_i = \left\{ x_1, x_2, x_3, \dots, x_n \right\} $$ for \( i = \left\{ 1, 2, 3, \dots, n \right\} \) the \(i\)th jackknife sample consists of the original sample data with the \(i\)th observation removed.

References

Wikipedia: Jackknife resampling

See also

Other jackknife functions: jack_hat()

Examples

n <- 5
#################################
# vector
#################################
x <- rnorm(n = n)
xstar <- jack(
  data = x
)
str(xstar)
#> List of 5
#>  $ : num [1:4] 0.25532 -2.43726 -0.00557 0.62155
#>  $ : num [1:4] -1.40004 -2.43726 -0.00557 0.62155
#>  $ : num [1:4] -1.40004 0.25532 -0.00557 0.62155
#>  $ : num [1:4] -1.4 0.255 -2.437 0.622
#>  $ : num [1:4] -1.40004 0.25532 -2.43726 -0.00557
#################################
# matrix
#################################
x1 <- rnorm(n = n)
x2 <- rnorm(n = n)
x3 <- rnorm(n = n)
X <- cbind(x1, x2, x3)
Xstar <- jack(
  data = X
)
str(Xstar)
#> List of 5
#>  $ : num [1:4, 1:3] -1.822 -0.247 -0.244 -0.283 0.629 ...
#>   ..- attr(*, "dimnames")=List of 2
#>   .. ..$ : NULL
#>   .. ..$ : chr [1:3] "x1" "x2" "x3"
#>  $ : num [1:4, 1:3] 1.148 -0.247 -0.244 -0.283 -0.554 ...
#>   ..- attr(*, "dimnames")=List of 2
#>   .. ..$ : NULL
#>   .. ..$ : chr [1:3] "x1" "x2" "x3"
#>  $ : num [1:4, 1:3] 1.148 -1.822 -0.244 -0.283 -0.554 ...
#>   ..- attr(*, "dimnames")=List of 2
#>   .. ..$ : NULL
#>   .. ..$ : chr [1:3] "x1" "x2" "x3"
#>  $ : num [1:4, 1:3] 1.148 -1.822 -0.247 -0.283 -0.554 ...
#>   ..- attr(*, "dimnames")=List of 2
#>   .. ..$ : NULL
#>   .. ..$ : chr [1:3] "x1" "x2" "x3"
#>  $ : num [1:4, 1:3] 1.148 -1.822 -0.247 -0.244 -0.554 ...
#>   ..- attr(*, "dimnames")=List of 2
#>   .. ..$ : NULL
#>   .. ..$ : chr [1:3] "x1" "x2" "x3"
#################################
# data frame
#################################
X <- as.data.frame(X)
Xstar <- jack(
  data = X
)
str(Xstar)
#> List of 5
#>  $ :'data.frame':	4 obs. of  3 variables:
#>   ..$ x1: num [1:4] -1.822 -0.247 -0.244 -0.283
#>   ..$ x2: num [1:4] 0.629 2.065 -1.631 0.512
#>   ..$ x3: num [1:4] -0.522 -0.0526 0.543 -0.9141
#>  $ :'data.frame':	4 obs. of  3 variables:
#>   ..$ x1: num [1:4] 1.148 -0.247 -0.244 -0.283
#>   ..$ x2: num [1:4] -0.554 2.065 -1.631 0.512
#>   ..$ x3: num [1:4] -1.863 -0.0526 0.543 -0.9141
#>  $ :'data.frame':	4 obs. of  3 variables:
#>   ..$ x1: num [1:4] 1.148 -1.822 -0.244 -0.283
#>   ..$ x2: num [1:4] -0.554 0.629 -1.631 0.512
#>   ..$ x3: num [1:4] -1.863 -0.522 0.543 -0.914
#>  $ :'data.frame':	4 obs. of  3 variables:
#>   ..$ x1: num [1:4] 1.148 -1.822 -0.247 -0.283
#>   ..$ x2: num [1:4] -0.554 0.629 2.065 0.512
#>   ..$ x3: num [1:4] -1.863 -0.522 -0.0526 -0.9141
#>  $ :'data.frame':	4 obs. of  3 variables:
#>   ..$ x1: num [1:4] 1.148 -1.822 -0.247 -0.244
#>   ..$ x2: num [1:4] -0.554 0.629 2.065 -1.631
#>   ..$ x3: num [1:4] -1.863 -0.522 -0.0526 0.543