Jackknife Estimation

Usage

# S4 method for DiversityIndex
jackknife(object, f = NULL)

Arguments

object: An R object (typically a DiversityIndex object).
f: A function that takes a single numeric vector (the leave-one-out values of do) as argument.

Value

If f is NULL (the default), jackknife() returns a named numeric

vector with the following elements:

original: The observed value of do applied to object.
mean: The jackknife estimate of mean of do.
bias: The jackknife estimate of bias of do.
error: he jackknife estimate of standard error of do.

If f is a function, jackknife() returns the result of f applied to the leave-one-out values of do.

Author

N. Frerebeau

Examples

## Data from Conkey 1980, Kintigh 1989
data("cantabria")

## Shannon diversity index
(h <- heterogeneity(cantabria, method = "shannon"))
#> [1] 3.269200 2.955298 2.491683 2.485604 2.329187

## Bootstrap resampling
bootstrap(h, f = NULL)
#>                  original     mean          bias      error
#> Altamira         3.269200 3.280402  0.0112025976 0.08079012
#> Cueto de la Mina 2.955298 2.975088  0.0197905305 0.14799321
#> El Juyo          2.491683 2.540509  0.0488266469 0.19898130
#> El Cierro        2.485604 2.486137  0.0005333296 0.21893775
#> La Paloma        2.329187 2.316002 -0.0131842316 0.25809208

bootstrap(h, f = summary)
#>                      Min.  1st Qu.   Median     Mean  3rd Qu.     Max.
#> Altamira         2.979042 3.228799 3.283048 3.278504 3.330980 3.513022
#> Cueto de la Mina 2.302014 2.866352 2.975547 2.969013 3.078826 3.351206
#> El Juyo          1.483755 2.410534 2.541252 2.526640 2.646816 3.189454
#> El Cierro        1.709473 2.358854 2.505909 2.492024 2.639654 3.010729
#> La Paloma        0.000000 2.163956 2.332882 2.312304 2.488794 2.914345

quant <- function(x) quantile(x, probs = c(0.25, 0.50))
bootstrap(h, f = quant)
#>                       25%      50%
#> Altamira         3.222216 3.274933
#> Cueto de la Mina 2.887177 2.983977
#> El Juyo          2.400082 2.532789
#> El Cierro        2.357496 2.505136
#> La Paloma        2.165553 2.343891

## Jackknife resampling
jackknife(h)
#>                  original     mean       bias      error
#> Altamira         3.269200 3.246457 -0.9779158 0.08145529
#> Cueto de la Mina 2.955298 2.932856 -0.9649994 0.15907033
#> El Juyo          2.491683 2.469588 -0.9500688 0.19670002
#> El Cierro        2.485604 2.462723 -0.9838709 0.22195573
#> La Paloma        2.329187 2.305876 -1.0023704 0.25266291

bootstrap(h, f = summary)
#>                      Min.  1st Qu.   Median     Mean  3rd Qu.     Max.
#> Altamira         2.985149 3.229306 3.285509 3.282846 3.338739 3.532789
#> Cueto de la Mina 2.464220 2.880741 2.981933 2.975875 3.070218 3.384048
#> El Juyo          1.740512 2.398534 2.521858 2.516742 2.646314 3.080476
#> El Cierro        1.589385 2.363075 2.510529 2.493531 2.639931 3.177633
#> La Paloma        1.039721 2.166085 2.351805 2.320512 2.510008 2.892961