bootstrapFP {bootstrapFP} | R Documentation |

## Bootstrap algorithms for Finite Population sampling

### Description

Bootstrap variance estimation for finite population sampling.

### Usage

```
bootstrapFP(
y,
pik,
B,
D = 1,
method,
design,
x = NULL,
s = NULL,
distribution = "uniform"
)
```

### Arguments

`y` |
vector of sample values |

`pik` |
vector of sample first-order inclusion probabilities |

`B` |
scalar, number of bootstrap replications |

`D` |
scalar, number of replications for the double bootstrap (when applicable) |

`method` |
a string indicating the bootstrap method to be used, see Details for more |

`design` |
sampling procedure to be used for sample selection. Either a string indicating the name of the sampling design or a function; see section "Details" for more information. |

`x` |
vector of length N with values of the auxiliary variable for all population units, only required if method "ppHotDeck" is chosen |

`s` |
logical vector of length N, TRUE for units in the sample, FALSE otherwise. Alternatively, a vector of length n with the indices of the sample units. Only required for "ppHotDeck" method. |

`distribution` |
required only for |

### Details

Argument `design`

accepts either a string indicating the sampling design
to use to draw samples or a function.
Accepted designs are "brewer", "tille", "maxEntropy", "poisson",
"sampford", "systematic", "randomSystematic".
The user may also pass a function as argument; such function should take as input
the parameters passed to argument `design_pars`

and return either a logical
vector or a vector of 0 and 1, where `TRUE`

or `1`

indicate sampled
units and `FALSE`

or `0`

indicate non-sample units.
The length of such vector must be equal to the length of `x`

if `units`

is not specified, otherwise it must have the same length of `units`

.

`method`

must be a string indicating the bootstrap method to use.
A list of the currently available methods follows, the sampling design they
they should be used with is indicated in square brackets.
The prefix "pp" indicates a pseudo-population method, the prefix "d"
represents a direct method, and the prefix "w" inicates a weights method.
For more details on these methods see Mashreghi et al. (2016).

"ppGross" [SRSWOR]

"ppBooth" [SRSWOR]

"ppChaoLo85" [SRSWOR]

"ppChaoLo94" [SRSWOR]

"ppBickelFreedman" [SRSWOR]

"ppSitter" [SRSWOR]

"ppHolmberg" [UPSWOR]

"ppChauvet" [UPSWOR]

"ppHotDeck" [UPSWOR]

"dEfron" [SRSWOR]

"dMcCarthySnowden" [SRSWOR]

"dRaoWu" [SRSWOR]

"dSitter" [SRSWOR]

"dAntalTille_UPS" [UPSWOR]

"wRaoWuYue" [SRSWOR]

"wChipperfieldPreston" [SRSWOR]

"wGeneralised" [any]

### Value

The bootstrap variance of the Horvitz-Thompson estimator.

### References

Mashreghi Z.; Haziza D.; Léger C., 2016. A survey of bootstrap methods in finite population sampling. Statistics Surveys 10 1-52.

### Examples

```
library(bootstrapFP)
### Generate population data ---
N <- 20; n <- 5
x <- rgamma(N, scale=10, shape=5)
y <- abs( 2*x + 3.7*sqrt(x) * rnorm(N) )
pik <- n * x/sum(x)
### Draw a dummy sample ---
s <- sample(N, n)
### Estimate bootstrap variance ---
bootstrapFP(y = y[s], pik = n/N, B=100, method = "ppSitter")
bootstrapFP(y = y[s], pik = pik[s], B=10, method = "ppHolmberg", design = 'brewer')
bootstrapFP(y = y[s], pik = pik[s], B=10, D=10, method = "ppChauvet")
bootstrapFP(y = y[s], pik = n/N, B=10, method = "dRaoWu")
bootstrapFP(y = y[s], pik = n/N, B=10, method = "dSitter")
bootstrapFP(y = y[s], pik = pik[s], B=10, method = "dAntalTille_UPS", design='brewer')
bootstrapFP(y = y[s], pik = n/N, B=10, method = "wRaoWuYue")
bootstrapFP(y = y[s], pik = n/N, B=10, method = "wChipperfieldPreston")
bootstrapFP(y = y[s], pik = pik[s], B=10, method = "wGeneralised", distribution = 'normal')
```

*bootstrapFP*version 0.4.6 Index]